# Patent

## Title
Ligands for odor receptors and olfactory neurons

## Abstract

The disclosure provides compounds useful as insect repellents and compositions comprising such repellents. The disclosure further provides insect traps and method for identifying ligands and cognates for biological molecules.

## Background
CROSS REFERENCE TO RELATED APPLICATIONS
This application is a continuation application of U.S. application Ser. No. 13/641,065, which is a national phase patent application of PCT/US2011/032804, filed Apr. 16, 2011, which claims priority to and benefit of U.S. Patent Application No. 61/325,236, filed Apr. 16, 2010, the disclosures of which are hereby incorporated by reference in their entireties.


TECHNICAL FIELD
The disclosure provides compounds useful as insect repellents and compositions comprising such repellents. The disclosure further provides compounds useful as insect attractants and compositions comprising such attractants. The disclosure further provides compounds useful as insect traps.
BACKGROUND
Numerous insects are vectors for disease. Mosquitoes in the genus Anopheles are the principle vectors of malaria, a disease caused by protozoa in the genus Trypanosoma. Aedes aegypti is the main vector of the viruses that cause Yellow fever and Dengue. Other viruses, the causal agents of various types of encephalitis, are also carried by Aedes spp. mosquitoes. Wuchereria bancrofti and Brugia malayi, parasitic roundworms that cause filariasis, are usually spread by mosquitoes in the genera Culex, Mansonia, and Anopheles. 
Horse flies and deer flies may transmit the bacterial pathogens of tularemia (Pasteurella tularensis) and anthrax (Bacillus anthracia), as well as a parasitic roundworm (Loa loa) that causes loiasis in tropical Africa.
Eye gnats in the genus Hippelates can carry the spirochaete pathogen that causes yaws (Treponema pertenue), and may also spread conjunctivitis (pinkeye). Tsetse flies in the genus Glossina transmit the protozoan pathogens that cause African sleeping sickness (Trypanosoma gambiense and T. rhodesiense). Sand flies in the genus Phlebotomus are vectors of a bacterium (Bartonella bacilliformis) that causes Carrion's disease (oroyo fever) in South America. In parts of Asia and North Africa, they spread a viral agent that causes sand fly fever (pappataci fever) as well as protozoan pathogens (Leishmania spp.) that cause Leishmaniasis.
SUMMARY
The methods of the disclosure provide an odor receptor optimized descriptor-based in silico screen of chemical space. The methods of the disclosure are useful for identifying ligands for odor receptors (Ors), greatly reducing the number of compounds needing to be physically tested through methods such as single-unit electrophysiology or cell imaging. In addition a very large number of odorants can be computationally predicted in a single run of a chemical informatics pipeline, thus enabling one to select the appropriate chemicals to use as ligand for target odor receptor based on other important considerations that can be easily determined such as volatility, solubility, toxicity, costs, environmental safety or other physico-chemical properties. As most approaches to ligand identification require physically testing odorants using expensive assays and purchasing large collections of test chemicals is very expensive, the in silico approaches described herein provides the ability to predict ligands with high accuracy greatly reduces the cost of identifying novel ligands.
The disclosure provides a method of identifying a ligand for a biological molecule comprising: (a) identifying a known ligand or set of known ligands for a biological molecule, or identifying a compound which causes a specific biological activity, (b) identifying a plurality of descriptors for the known ligand or compound, (c) using a Sequential Forward Selection (SFS) descriptor selection algorithm to incrementally create a unique optimized descriptor subsets from the plurality of descriptors for the known ligand or compound, (d) identifying a putative ligand or compound that best-fits the unique optimized descriptor subset, and (e) testing the putative ligand or compound in a biological assay comprising the biological molecule wherein a change in activity of the biological molecule compared to the molecule without the putative ligand is indicative of a ligand the interacts with the biological molecule. The method above can be applied to any number of biological molecules that have a binding cognate. For example, the biological molecule can be a receptor, a ligand gated ion channel or G-protein coupled receptor. In a specific embodiment, the receptor is an odor receptor. In another embodiment, the receptor is expressed in a cell. In any of the foregoing embodiments, the plurality of descriptors are selected from the group consisting of distance metrics, descriptor sets, and activity thresholds. Further, in any of the foregoing embodiments, the distance metrics are selected from the group consisting of Euclidean, Spearman, and Pearson coefficients. In any of the foregoing embodiment, the descriptor sets are selected from Dragon, Cerius2, and a combined Dragon/Cerius2 set. In yet another embodiment, which can be implemented and used with any of the foregoing embodiments, two activity threshold methods are compared. In a further embodiment, the activity threshold comprises spike activity cutoffs and a cluster-based cutoff. In yet another embodiment of any of the foregoing the identifying further comprises selecting a putative ligand or compound with in a desired Euclidian distance of the known ligand or biological compound. For example, the Euclidian distance is about 0.001 to about 6.60 from a known ligand or cluster of ligands in chemical space. In another embodiment, the ligand binds to a CO2 receptor and wherein the ligand has a Euclidian distance of about 0.001 to 6.60 from a known ligand for a CO2 receptor. In yet another embodiment, the putative ligand is selected from a compound in Table 9 and 10. In another embodiment of any of the foregoing the descriptors are selected from the descriptors in Table 7 and 8. The methods described above can utilize a known ligand or set of known ligands identified through electrophysiology, imaging assays, or binding assays. The methods above can be used to screen a library of compounds. The method may be fully automated or may output the putative ligand or compound to a user who may then perform a biological assay. The biological assay can use various indicators for determining a ligand (e.g., an agonist or antagonist ligand) including a biological assay measuring a change in spike frequency, florescence intensity, or binding affinity. The odor receptor may be a vertebrate or invertebrate odor receptor. In yet another embodiment of any of the foregoing, the putative ligands or compounds are soluble ligands or compounds and the receptor is a gustatory receptor expressed by an invertebrate species or a gustatory receptor neurons present in an invertebrate. In yet another embodiment of any of the foregoing, the putative ligands or compounds the receptor is a gustatory receptor expressed by an invertebrate species or a gustatory receptor neurons present in an invertebrate. In yet another embodiment of any of the foregoing, the putative ligands or compounds the receptor is a gustatory receptor expressed by an invertebrate species or a gustatory receptor neurons present in an invertebrate. In yet another embodiment of any of the foregoing, the putative ligands or compounds the receptor is a gustatory receptor expressed by an mammal species or a gustatory receptor neurons present in an mammal. In yet another embodiment of any of the foregoing, the putative ligands or compounds the receptor is a gustatory receptor expressed by an mammal species or a gustatory receptor neurons present in an mammal. In yet another embodiment of any of the foregoing, the putative ligands or compounds the receptor is a gustatory receptor expressed by an mammal species or a gustatory receptor neurons present in an mammal.
The disclosure also provides a ligand or compound identified by the method of any of the foregoing claims. In one embodiment, the compound/ligand is set forth in Table 4, 6, 9 and 10. The ligand or compound can be an odor receptor ligand having a desired Euclidian distance from a cluster of known ligands defined by structural-data information wherein the compound reversibly or irrevisibly binds an odor receptor.
The disclosure also provides use of a ligand or compound identified by the methods of the disclosure or a ligand or compound in Table 4, 6, 9 or 10 to lure insect species into traps by virtue of activating odor receptors or odor receptor neurons. In an embodiment, the trap is suction based, light based, electric current based. In another embodiment, the ligand or compound is used the preparation of a topical cream, spray or dust present within or near a trap entrance. The ligand or compound can be used in a vapor emitted from vaporizers, treated mats, treated pods, absorbed material, cylinders, oils, candles, wicked apparatus, fans, within or near trap entrances. The ligand or compound can be used a repellant or attractant. The repellant or attractant can be used in a cream, lotion, spray, dust, vapor emitter, candle, oil, wicked apparatus, fan, or vaporizer. The ligand or compound can be used to affect mating behavior.
The disclosure also provides a composition comprising a ligand or compound of as described above in a cream, oil, lotion, spray, perfume, cologne, fragrance, deodorant, masking agent, candle, vaporizer, and the like.
The methods of the disclosure can also be used to identify food additives of flavorants.
The details of one or more embodiments of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the disclosure will be apparent from the description and drawings, and from the claims.



DESCRIPTION OF DRAWINGS
FIG. 1 shows a schematic of a method of the disclosure used to identify an optimized descriptor subsets for each Or.
FIG. 2 shows a variety of selection method combinations.
FIGS. 3A-3T show diagrams of compound activity classification through activity clustering. Compounds were clustered based on difference in activity. Compounds below certain squares, indicate cut points. Diagrams are provided for receptors: Or7a (FIG. 3A); Or9a (FIG. 3B); Or22a (FIG. 3C); Or35a (FIG. 3D); Or49b (FIG. 3E); Or59b (FIG. 3F); Or10a (FIG. 3G); Or19a (FIG. 3H); Or43b (FIG. 3I); Or47a (FIG. 3J); Or67a (FIG. 3K); Or67c (FIG. 3L); Or82a (FIG. 3M); Or85a (FIG. 3N); Or85b (FIG. 3O); Or98a (FIG. 3P); Or2a (FIG. 3Q); Or23a (FIG. 3R); Or43a (FIG. 3S); and Or85f (FIG. 3T).
FIG. 4 shows a schematic of selecting highest scoring optimization methods.
FIG. 5 is a graph comparing APoA values.
FIGS. 6A-6T show analyses of APoA for individual Odor receptors. Analyses are provided for receptors: Or7a (FIG. 6A); Or9a (FIG. 6B); Or22a (FIG. 6C); Or35a (FIG. 6D); Or10a (FIG. 6E); Or19a (FIG. 6F); Or43b (FIG. 6G); Or47a (FIG. 6H); Or49b (FIG. 6I); Or59b (FIG. 6J); Or82a (FIG. 6K); Or85a (FIG. 6L); Or67a (FIG. 6M); Or67c (FIG. 6N); Or85b (FIG. 6O); Or98a (FIG. 6P); Or2a (FIG. 6Q); Or23a (FIG. 6R); Or43a (FIG. 6S); and Or85f (FIG. 6T).
FIG. 7 shows a comparison of highest molecular descriptor APoA for each Or.
FIGS. 8A-8T show clustering of drosophila odorants by optimized descriptor subsets. Clustering diagrams are provided for receptors: Or7a (FIG. 8A); Or9a (FIG. 8B); Or22a (FIG. 8C); Or35a (FIG. 8D); Or49b (FIG. 8E); Or59b (FIG. 8F); Or10a (FIG. 8G); Or19a (FIG. 8H); Or43b (FIG. 8I); Or47a (FIG. 8J); Or67a (FIG. 8K); Or67c (FIG. 8L); Or82a (FIG. 8M); Or85a (FIG. 8N); Or85b (FIG. 8O); Or98a (FIG. 8P); Or2a (FIG. 8Q); Or23a (FIG. 8R); Or43a (FIG. 8S); and Or85f (FIG. 8T).
FIG. 9A shows a computational validation of Drosophila optimized descriptor sets.
FIG. 9B shows high-throughput flowchart for in silico screen of each Or with >240,000 compounds.
FIGS. 10A-10I show electrophysiology validations of drosophila in silico screen for the following receptors: Or7a (FIG. 10A); Or10a (FIG. 10B); Or22a (FIG. 10C); Or47a (FIG. 10D); Or49b (FIG. 10E); Or59b (FIG. 10F); Or85a (FIG. 10G); Or85b (FIG. 10H); and Or98a (FIG. 10I).
FIG. 11 shows an electrophysiology testing for drosophila “false negative” rates of prediction.
FIGS. 12A-12F show table 2, drosophila compounds tested for activity: Or2a-Or49b. Compounds tested for activity: Drosophila Or2a-Or49b. Chemical name, a 2-D structural image, and distance measure are listed for each tested compound. All distances are Euclidean and represent the distance between each compound and their closest known active by optimized descriptor values. Known active compounds from the training set are the top 12, 7, 13, 5, 9 and 3 compounds respectively in each column, predicted compounds that were validated as actives are appropriately boxed, inhibitors are appropriately boxed, and inactive compounds are boxed.
FIGS. 13A-13F show table 3 drosophila compounds tested for activity: Or59b-Or98a. Compounds tested for activity: Drosophila Or59b-Or98a. List of compounds that were tested using electrophysiology for each Or. Chemical name, a 2-D structural image, and distance measure are listed for each tested compound. All distances are Euclidean and represent the distance between each compound and their closest known active by optimized descriptor values. Known active compounds from the training set are the top 12, 7, 13, 5, 9 and 3 compounds respectively in each column, predicted compounds that were validated as actives are appropriately boxed, inhibitors are appropriately boxed, and inactive compounds are boxed.
FIG. 14 shows validation accuracy for predicted drosophila ligands.
FIGS. 15A-15C show ligand prediction from neuronal activity. FIG. 15A shows an optimized descriptor based cluster for Or42b. FIG. 15B shows an electrophysiology validation for various compounds. FIG. 15C is a 3-D structural image of a compound from the electrophysiology validation.
FIGS. 16A and 16B depict ligand prediction from narrowly tuned Ors. FIG. 16A shows flow diagrams for (i) predicting initial screening compounds from single strong ligand, (ii) identifying additional activators through electrophysiology validation, and (iii) predicting additional activating compounds from single strong ligand and validated activators. FIG. 16B shows an example for Or82a of (i) an all descriptor cluster, (ii) electrophysiology validation, and (iii) an optimized descriptor based cluster.
FIGS. 17A-17AK show clustering mammalian odorants by optimized descriptor subsets for MOR1-1 (FIG. 17A); MOR106-1 (FIG. 17B); MOR139-1 (FIG. 17C); MOR162-1 (FIG. 17D); MOF189-1 (FIG. 17E); MOR2-1 (FIG. 17F); MOR107-1 (FIG. 17G); MOR129-1 (FIG. 17H); MOR170-1 (FIG. 17I); MOR184-1 (FIG. 17J); MOR203-1 (FIG. 17K); MOR204-6 (FIG. 17L); MOR136-1 (FIG. 17M); MOR223-1 (FIG. 17N); MOR185-1 (FIG. 17O); MOR260-1 (FIG. 17P); MOR207-1 (FIG. 17Q); MOR273-1 (FIG. 17R); MOR250-1 (FIG. 17S); MOR256-17 (FIG. 17T); MOR261-1 (FIG. 17U); MOR268-1 (FIG. 17V); MOR277-1 (FIG. 17W); MOR30-1 (FIG. 17X); MOR258-1 (FIG. 17Y); MOR259-1 (FIG. 17Z); MOR271-1 (FIG. 17AA); MOR272-1 (FIG. 17AB); MOR33-1 (FIG. 17AC); MOR37-1 (FIG. 17AD); MOR40-1 (FIG. 17AE); MOR41-1 (FIG. 17AF); MOR5-1 (FIG. 17AG); OR1A1 (FIG. 17AH); OR2J2 (FIG. 17AI); OR2W1 (FIG. 17AJ); and OR5P3 (FIG. 17AK).
FIGS. 18A-18G show computational validation of mammalian OR compound clustering for MOR41-1 (FIG. 18A); MOR271-1 (FIG. 18B); MOR203-1 (FIG. 18C); MOR272-1 (FIG. 18D); MOR139-1 (FIG. 18E); OR1A1 (FIG. 18F); and OR2W1 (FIG. 18G).
FIG. 19 shows clustering CO2 neuron activating odorants from training set 1 by optimized descriptor subsets.
FIG. 20 shows clustering CO2 neuron activating odorants from training set 2 by optimized descriptor subsets.
FIGS. 21A-21W show accumulated percentage of actives and activity based cluster analysis. FIG. 21A shows a representative example for an Accumulated Percentage of Actives (APoA) calculation. Green box=active, grey box=inactive. To calculate APoA each active compound was iteratively used as a reference active. Compounds are sorted based upon their increasing descriptor based distance from reference active, and the APoA calculated for each of the other compounds as a ratio of the number of actives over the total number of compounds considered from the reference compound. This process was repeated using each active odorant as a reference active. Reference compound APoAs were averaged to a single mean APoA value. The higher the APoA value while considering a fixed number of nearest neighboring compounds, the greater the proportion of active compounds clustered together. FIG. 21B shows a plot of the mean APoA calculated values calculated using each molecular descriptor method, averaged across all 20 Ors for Dragon, Cerius2, MCS and Atom Pair. FIG. 21C shows coloured cells mark the method that clusters active ligands best as determined by the highest Area-Under-Curve (AUC) values. E=Euclidean, S=Spearmans coefficient, and T=Tanimoto coefficient. FIG. 21D-21S show compounds clustered based on activity of Or, such as Or7a (FIG. 21D); Or9a (FIG. 21E); Or22a (FIG. 21F); Or35a (FIG. 21G); Or49b (FIG. 21H); Or59b (FIG. 21I); Or10a (FIG. 21J); Or19a (FIG. 21K); Or43b (FIG. 21L); Or47a (FIG. 21M); Or67a (FIG. 21N); Or67c (FIG. 21O); Or82a (FIG. 21P); Or85a (FIG. 21Q); Or85b (FIG. 21R); and Or98a (FIG. 21S). Activity color scale is indicated. Branches marked with small green squares (either 1 or 2) were considered as actives. FIGS. 21T-21W show dependent cluster analysis for Ors that have only weak ligands as done in FIGS. 21D-21S, including Or2a (FIG. 21T); Or23a (FIG. 21U); Or43a (FIG. 21V); and Or85f (FIG. 21W).
FIG. 22 shows that vapor pressure possibly affects ligand-Odor receptor activation. Vapor pressures and activities (in spikes/sec) were plotted for validated odorant predictions. Compounds are divided into four classes based upon compound activity and vapor pressure values.
FIGS. 23A-23V show predicted breadth of tuning for collected compounds in Odorant receptors, such as Or23a (FIG. 23A); Or82a (FIG. 23B); Or49b (FIG. 23C); Or92a (FIG. 23D); Or42b (FIG. 23E); Or22a (FIG. 23F); Or59b (FIG. 23G); Or43b (FIG. 23H); Or85a (FIG. 23I); Or85f (FIG. 23J); Or19a (FIG. 23K); Or67c (FIG. 23L); Or85b (FIG. 23M); Or7a (FIG. 23N); Or2a (FIG. 23O); Or67a (FIG. 23P); Or43a (FIG. 23Q); Or35a (FIG. 23R); Or98a (FIG. 23S); Or9a (FIG. 23T); Or47a (FIG. 23U); and Or10a (FIG. 23V). Compounds from the collected compound library that have been catalogued as plant, human and total collected volatiles were ranked according to their relative distance from the compound with highest activity. Frequency distribution of compounds within the top 15% is plotted to generate predicted breadth of tuning curves. X-axes are in logarithmic scale.



DETAILED DESCRIPTION
As used herein and in the appended claims, the singular forms “a,” “and,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “an insect” includes a plurality of such insects and reference to “the compound” includes reference to one or more compounds, and so forth.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this disclosure belongs. Although any methods and reagents similar or equivalent to those described herein can be used in the practice of the disclosed methods and compositions, the exemplary methods and materials are now described.
Also, the use of “or” means “and/or” unless stated otherwise. Similarly, “comprise,” “comprises,” “comprising” “include,” “includes,” and “including” are interchangeable and not intended to be limiting.
It is to be further understood that where descriptions of various embodiments use the term “comprising,” those skilled in the art would understand that in some specific instances, an embodiment can be alternatively described using language “consisting essentially of” or “consisting of.”
All publications mentioned herein are incorporated herein by reference in full for the purpose of describing and disclosing the methodologies, which are described in the publications, which might be used in connection with the description herein. The publications discussed above and throughout the text are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the inventors are not entitled to antedate such disclosure by virtue of prior disclosure.
The methods of the disclosure allows intelligent and rapid screening of untested volatile chemical space by computationally identifying important characteristics shared between known active compounds. Also provided are compounds identified by the methods of the disclosure for use as insect repellents and attractants.
The olfactory system can detect and discriminate amongst an extremely large number of volatile compounds in the environment, and this is critical for important behaviors like finding food, finding mates, and avoiding predators. To detect this wide variety of volatiles, most organisms have evolved extremely large families of receptor genes that typically encode 7-transmembrane proteins that are expressed in the olfactory neurons. Little is known, however, about how small volatile molecules are detected and represented with high levels of specificity and sensitivity by the activities of odor receptor repertoires. The disclosure is able to greatly increase this understanding, and improve the ability to manipulate the olfactory based behavior of an organism. Additionally the computational method can be used to identify novel fragrances for individual odor receptors, which can have use in the fragrance, food, beverage, cleaning and other volatile chemical related industries.
Most blood feeding insects, including mosquitoes, sandflies, Testse flies, use olfactory cues to identify human hosts. This group of hematophagous insects can transmit a wide assortment of deadly human diseases that together cause more suffering and deaths globally than any other disease condition. Diseases transmitted by such insects include malaria, dengue fever, yellow fever, West Nile virus, filariasis, river blindness, epidemic polyarthritis, Leshmaniasis, trypanosomiasis, Japanese encephalitis, St. Louis Encephalitis amongst others.
Traditional vector control methods often involve the heavy use of chemical insecticides that are harmful to the environment and often to human health. Moreover, insects can develop resistance to these chemicals, suggesting that there is a need to identify novel ways of insect control that are effective, cheap, and environmentally friendly. Integrating methods that inhibit vector-human contact, such as vector control and the use of insect repellents, bednets, or traps, may play a complementary and critical role in controlling the spread of these deadly diseases.
In insects host-odor cues, among others, are detected by olfactory receptor neurons (ORNs) that are present on the surface of at least two types of olfactory organs, the antennae and the maxillary palps. The antenna is the main olfactory organ and its surface is covered by hundreds of sensilla, each of which is innervated by the dendrites of 1-5 ORNs. Odor molecules pass through pores on the surface of sensilla and activate odor receptor proteins present on the dendritic membranes of the ORNs.
The odor receptor (Or) gene family in insects was first identified in D. melanogaster. It comprises a highly divergent family of 60 Odor receptor (Or) genes that encode proteins predicted to contain seven trans-membrane regions.
One of the most important host-seeking cues for hematophagous insects is CO2. The CO2 receptor was first identified in D. melanogaster. This receptor comprises two proteins, Gr21a and Gr63a, which are encoded by two members of a large Gustatory receptor (Gr) gene family that is distantly related in sequence to the Or genes. Both Gr21a and Gr63a are extremely well conserved in sequence across several insect species. Orthologs for both Gr21a and Gr63a have been identified in An. gambiae and Ae. aegypti. Moreover, both mosquitoes possess a third gene that is closely related to Gr21a. The three An. gambiae homologs AgGr22, AgGr23 and AgGr24 are co-expressed in ORNs of the maxillary palp. Functional expression studies in Drosophila have demonstrated that they are CO2 receptors as well.
Odor responses of ORNs on the surface of the antennae and maxillary palps have been studied using two separate techniques. Whole organ recordings called electroantennograms (EAGs) and electropalpograms (EPGs) have been used to detect the aggregate electrical activities from a large number of neurons in response to odors. A more sensitive and exact method has also been used to examine the functional properties of olfactory neurons within a single sensillum, and neurons that respond to behaviourally important ligands such as CO2, ammonia, phenols, 1-octen-3-ol, lactic acid, and carboxylic acids have been identified.
Because mosquitoes rely on their sense of smell to identify human odors, olfactory system function is a prime target to modify host-seeking behaviour. The kairomone CO2 is used as bait by several mosquito traps that are currently sold on the market. In some instances an additional odor, usually 1-octen-3-ol, is also included to increase the efficiency of mosquito catches. Identification of more potent attractant odors, or more efficacious odor blends are required to further improve the efficiency of these CO2 traps. Development of cheap CO2-free traps may be of particular importance since generating CO2 in a trap is problematic.
In a complementary fashion, blocking of insect odor receptors may be effective in masking human hosts, or may even work as repellents. There has been a great interest to identify novel classes of volatile compounds that can block mosquito receptors that detect kairomones like CO2.
Volatile chemical space is immense. Odors in the environment that have been catalogued in some plant sources alone number more than two thousand. A very small proportion of chemical space has been systematically tested for the ability to activate or inhibit individual odor receptors, and a very small fraction of odor receptors, whose sequences are known, have been tested for activity. The complete 3-D structures of odor receptor proteins have not yet been determined, thus modeling of odor-protein interactions is not yet possible except in rare instances. Furthermore, were a 3-D receptor structure to become available, application of one odor-receptor interaction to study others may be confounded by the possibility of multiple ligand binding sites in a single receptor, as well as the sequence divergence amongst different odor receptors.
Odor receptor responses to odorants have been tested in vivo in the organism of interest predominately through two separate techniques. One approach involves whole organ recordings called electroantennograms (EAGs), eletropalpograms (EPGs), and electroolfactograms (EOGs) which have been used to detect the aggregate electrical activities from a large number of olfactory neurons in response to odors. This technique does not allow for differentiation between odor receptor neuron responses and thus does not allow for identification of individual odor receptor responses to an odorant. A more sensitive and precise technique called single unit electrophysiology allows for individual odor receptor neuron responses to odors to be quantitatively measured. This technique either requires the odor receptor map to have been previously established by molecular tools or use of an “empty-neuron” system that utilizes a transgenic approach.
In Drosophila melanogaster a mutant antennal neuron called the “empty neuron” has been identified. The system uses a mutant strain of D. melanogaster in which a chromosomal deletion has resulted in the loss of the Or22a gene. The Or22a gene product is usually expressed in an easily identifiable and accessible neuron type in the antenna called ab3A, which now does not express an odor receptor and therefore does not respond to any odors. An exogenous Or gene can then be functionally expressed in this mutant “empty neuron” genetic background using the promoter of Or22a. Responses to a diverse set of odorants can be recorded using single-sensillum electrophysiology. Through iteratively inserting and testing Or genes, electrophysiological responses of 24 Ors to a preliminary set of 110 diverse compounds was determined, as well as 21 additional Or genes to a set of 27 compounds. The compound sets consisted of volatile compounds with varying functional groups and hydrocarbon chain lengths. It has also been demonstrated that expression of functional odor receptors from other organisms is possible in the Drosophila “empty neuron” system. The level of throughput of this system is  ̃100s to 1000s of odors in one year.
Additionally, other in vivo techniques have been used involving testing individual odor receptors of interest through transgenic expression in other organisms. Heterologous expression of Odor receptor genes from many species has been performed in Xenopus oocytes and Human Embryonic Kidney (HEK) 293 cells. Exposure of these cells to volatile compounds allows for a quantitative measure of response.
While these systems do provide a means to specifically express an odor receptor and obtain a quantitative measure of activation to a panel of odorants, their use is a very time consuming, expensive, and difficult process. Use of the “empty neuron” system and other heterologous expression approaches require transgenic fly lines to be produced or cDNA expression constructs made for each odor receptor to be tested. It has also been debated whether these expression systems produce wild type responses in all cases, as some cell specific components such as odorant binding proteins (OBPs) may be absent. Additionally all systems require the requirement of purchasing odors, diluting them, and performing the technically challenging testing of odorants.
In previous studies, individual odor receptors have sometimes been found to recognize compounds of similar functional groups containing similar hydrocarbon chain lengths. In addition it has also been shown that many Ors can be responsive to multiple distinct groups of structurally similar compounds. This property of odor receptors recognizing structurally similar compounds provides a framework for using cheminformatic similarity measures to predict novel active odorants.
Molecular descriptors are able to describe the structure of molecules through computationally derived values, which represent zero, one, two, or three-dimensional information of a compound. These descriptor type dimensionalities confer molecular information through classes such as constitutional, structural fragment, topographic, or spatial information, respectively.
Comparison of molecular descriptors to identify commonalities between highly active odorant structures has recently proven to be highly beneficial. In species where a specific behaviour, such as avoidance, has been tested against a panel of odors it is possible to use molecular descriptors to identify novel potential ligands using the known actives as a training set. For instance, the structure of N,N-diethyl-m-toluamide (DEET) was recently used to create a focused structural library, which was computationally ranked using Artificial Neural Networks (ANNs), and used to identify a more potent mosquito repellent. In another study a group analyzed Drosophila ORN responses to odors to identify activation metrics that were used to predict and test ligands from a small set of 21 compounds (Schmuker et al., 2007). The success rate of this strategy, as established by applying a neuronal firing rate cut-off of 50 spikes/sec to categorize activators, was <25%. Most recently a multi species approach was used to identify molecular descriptors that were important in compounds involved in olfaction however predictions were not possible. In another study by the same lab, an electronic nose was trained such that when presented with a novel odor it could predict whether or not the odor would activate an individual Or.
The methods of the disclosure allows intelligent and rapid screening of untested volatile chemical space and chemical libraries by computationally identifying important characteristics shared between known active compounds, circumventing many of the previously described obstacles.
The disclosure provides a chemical informatics method that identifies important structural features shared by ligands such as activating odors for individual odor receptors or olfactory neurons and utilizes these important features to screen large libraries of compounds in silico for novel ligands. These important structural features can also be used to increase understanding of breadth of tuning for each cognate of a ligand such as an odor receptor in chemical space and perform reverse chemical ecology in silico.
Although the methods of the disclosure have been exemplified using odor receptor and volatile chemical species. The method is also predicatable to taste receptors, g-protein coupled receptors, ion gated channels, ligand gated channels and the like.
The disclosure provides methods for identifying and the identified compositions comprising volatile odorants that can modulate the electrophysiological response of neuron in various insect disease vectors including Drosophila melanogaster, Culex quinquefasciatus, An. gambiae and Aedes aegypti mosquitoes. In some embodiment, the odorants can completely inhibit the electrophysiological response of the neuron at very low concentrations.
The odorants of the disclosure provide new and useful compositions for insect repellents, masking agents and traps. The compounds of the disclosure are useful in small quantities, can be delivered in multiple forms like vapors and lotions, are economical, environmentally friendly, and are present in natural sources.
Based upon the data and chemical odorants identified herein, additional odorants can be identified using the structural information of the odorants, in silico modeling and screening and biological assays.
The disclosure provides a group of volatile chemicals that can be used to modify host-seeking behaviour by stimulating or inhibiting odor and taste receptors.
The compounds and compositions of the disclosure can be used as antagonist to mask the chemo attractant activity for a particular odor receptor. Alternatively, the certain compounds may at as agonist in which they activate the receptor and stimulate the neuron. In such instances the compounds and compositions can be used as attractants alone or in combination with other materials depending upon the subject and purpose (e.g. an insecticide, trap, or other mechanical, electrical or chemical that kills the insect or prevents its escape).
An antagonist refers to a compound the can reversibly or irreversibly inhibit that activity of a sensing neuron upon exposure to the compound such that the neuron ORN cannot properly signal upon a change in odor levels.
Structure-based clustering can be used to identify compounds useful in compositions of the disclosure. The algorithm can include linkage clustering to join compounds into similarity groups, where every member in a cluster shares with at least one other member a similarity value above a user-specified threshold.
The identified compounds can then be assayed to identify their biological activity using the electrophysiology measurements described below. For example, a compound can be contacted with a CO2 receptor neuron and changes in the electrical signal measured. Alternatively, the compounds may be screened in a Drosophila CO2 avoidance chamber.
The disclosure provides chemicals that can be used as insect repellents and/or masking agents by virtue of their property to block a critical component of the host odor cue. The compounds are effective if they are capable of inhibiting the electrophysiological response of the neuron.
The volatile compounds of the disclosure have masking and repellant effects by impairing the ability to find a host via long-range cues emitted from a typical target or subject (e.g., human breath).
The disclosure provides a method of controlling insect attraction to a subject, the method comprising the step of inhibiting receptor activation (e.g., CO2 sensing gustatory receptors) in the insect or over stimulating the receptor with an antagonist (or a combination of antagonists).
In another embodiment, this disclosure provides a method of inhibiting, preventing or reducing the incidence of insect-borne disease in a subject, the method comprising the step of over stimulating or antagonizing a receptor in an insect with a compounds or combination of compounds, wherein the receptor response is modified and attraction to the subject inhibited, thereby inhibiting, preventing or reducing the incidence of insect-borne disease in a subject.
In one embodiment, the disease is malaria, dengue, yellow fever, river blindness, lymphatic filariasis, sleeping sickness, leishmaniasis, epidemic polyarthritis, West Nile virus disease or Australian encephalitis.
The compounds may be used alone or in combination with other agents. The compounds of the disclosure may be combined with additional active agent, insecticides and the like in traps to reduce the presence of amount of an insect in the environment. For example, compounds of the disclosure may be used in combination with insect traps (e.g., tape, combustibles, electric traps).
In yet a further embodiment, the compounds may be formulated for application to the skin, clothing or other material. The compounds of the disclosure can “mask” the location of a subject by antagonizing the receptor neurons of an insect etc. thereby inhibiting the ability to locate a prey.
For example, the compounds of the disclosure may be used as repellents or in compositions comprising said repellent compounds and the use of such repellent compounds and compositions in controlling pests.
Liquid formulations may be aqueous-based or non-aqueous (e.g., organic solvents), or combinations thereof, and may be employed as lotions, foams, gels, suspensions, emulsions, microemulsions or emulsifiable concentrates or the like. The formulations may be designed to be slowly release from a patch or canister.
The compositions may comprise various combinations of compounds as well as varying concentrations of the compound depending upon the insect to be repelled or masked, the type of surface that the composition will be applied to, or the type of trap to be used. Typically the active ingredient compound of the disclosure will be present in the composition in a concentration of at least about 0.0001% by weight and may be 10, 50, 99 or 100% by weight of the total composition. The repellent carrier may be from 0.1% to 99.9999% by weight of the total composition. The dry formulations will have from about 0.0001-95% by weight of the pesticide while the liquid formulations will generally have from about 0.0001-60% by weight of the solids in the liquid phase.
As mentioned above, the compositions may be formulated for administration to a subject. Such formulations are typically administered to a subject's skin. The composition may also be formulated for administration to garments, belts, collars, or other articles worn or used by the subject from whom insects are to be repelled. The formulation may be applied to bedding, netting, screens, camping gear and the like. It will be recognized that the application of the compositions and compounds of the disclosure do not only include human subjects, but include canines, equines, bovines and other animals subject to biting insects. For topical application, the formulation may take the form of a spray formulation or a lotion formulation.
The compounds according to the disclosure may be employed alone or in mixtures with one another and/or with such solid and/or liquid dispersible carrier vehicles as described herein or as otherwise known in the art, and/or with other known compatible active agents, including, for example, insecticides, acaricides, rodenticides, fungicides, bactericides, nematocides, herbicides, fertilizers, growth-regulating agents, and the like, if desired, in the form of particular dosage preparations for specific application made therefrom, such as solutions, emulsions, suspensions, powders, pastes, and granules as described herein or as otherwise known in the art which are thus ready for use.
The repellent compounds may be administered with other insect control chemicals, for example, the compositions of the invention may employ various chemicals that affect insect behaviour, such as insecticides, attractants and/or repellents, or as otherwise known in the art. The repellent compounds may also be administered with chemosterilants.
In yet another aspect, the volatile compounds of the disclosure may be emitted from vaporizers, treated mats, cylinders, oils, candles, wicked apparatus, fans and the like. Liquid source that can evaporate to form vapors may be used in barns, houses, or patios.
The disclosure also provides chemicals that can be used as bait to lure insects to traps by virtue of activating neurons. An advantage of these odorants will be their ability to be delivered in an economical and convenient form for use with traps. This function can be achieved by applying or locating the chemotractant compound of the disclosure near a suction based, or light based, or electric current based or other forms of trapping apparatus.
The disclosure provides a structural basis of odorant molecule interaction with odor receptors through a novel chemical informatics platform. The disclosure provides a method to identify molecular structural properties that are shared between the activating odorants (actives) for an individual odor receptor. By identifying the molecular features shared by actives, the disclosure provides a system to perform in silico screens of large chemical space (100s of thousands to millions) to predict novel ligands for odor receptors or odor receptor neurons. This method can be applied in virtually any species where a training set of odorant responses is known for individual receptor or cellular level. The disclosure demonstrates this using a single unit electrophysiology to test a subset of the predictions in vivo. The data demonstrate that the method is very successful in predicting novel ligands.
The disclosure demonstrates the method can be modified to be able to predict ligands for narrowly-tuned receptors and neurons that are thought to be highly specialized, like pheromone receptors. In addition olfactory neurons whose response profiles are known, but whose odor receptors have not yet been decoded are provided. The method is also able to predict odorant ligands for two distinctly different classes of odor receptors. Insect odor receptors are proposed to be 7 transmembrane GPCR like proteins with inverse orientation in the membrane that function as either heteromeric ligand gated ion channels or cyclic-nucleotide activated cation channels. Mammalian odor receptors on the other hand are true GPCRs. The method is able to predict ligands for both insect and mammalian odor receptor classes. In addition to predicting ligands the disclosure also allows investigation of the coding of each tested receptor or receptor neuron in chemical space consisting of plant volatiles, fragrances and human volatiles.
The CO2 receptor is believed to be very important in host seeking behaviour in mosquitoes. There are several commercially available approaches that use CO2 as a lure to trap insects. However, these current approaches have several drawbacks. Many traps require the use of a CO2 tank or dry ice to produce the CO2 lure plume. These CO2 tanks are large and heavy, making the trap itself cumbersome. Dry ice melts quickly and must be replaced often. A much smaller and longer lasting trapping approach would be advantageous. Identification of odors that could specifically activate this receptor could provide a very effective means of luring mosquitoes into traps. The approach can be used to identify odors that activate individual receptors, such as the CO2 receptor.
Since different odor receptors can respond to vastly differing compound shapes and sizes it is unlikely that the full collection of molecular descriptors would be optimal for all receptors. Depending upon the unique structural features of active odors certain molecular descriptors may be better suited at describing characteristics of activating compounds for an individual receptor, and such descriptors can be identified from much larger sets by dimensionality reduction. Thus it is possible to greatly improve Or-specific descriptor space by identifying specific molecular descriptors from amongst the large collection that were best suited for each Or.
The disclosure provides a method of computationally screening a vast number of compounds to predict ligands (activators or inhibitors) for individual receptors or receptor expressing cells, wherein a known ligand or set of known ligands for a receptor or receptor expressing cell, either identified through electrophysiology, imaging assays, or binding assays, are used as a training set for selecting optimized molecular descriptors, which can subsequently be used to screen a large collection of untested compounds computationally to identify compounds that are structurally related to the known ligands, outputting the identified putative ligands to a user and exposing a receptor or receptor expressing cell to the putative ligand and determining either a change in spike frequency, florescence intensity, or binding affinity in the receptor or receptor expressing cell, wherein a change compared to baseline is indicative of a ligand for the receptor or receptor expressing cell.
The disclosure also provides a method of computationally screening a vast number of compounds to predict ligands (activators or inhibitors) for individual receptors or receptor expressing cells that have only one known strong activator or inhibitor, either identified through electrophysiology, imaging assays or binding assays, wherein a single known ligand from a receptor or receptor expressing cell is used to identify the structurally closest compounds in a chemical space made using several or all available structural descriptors, outputting the identified putative ligands to a user and exposing a receptor or receptor expressing cell to the putative ligand and determining either a change in spike frequency, florescence intensity, or binding affinity in the receptor or receptor expressing cell, wherein a change compared to baseline is indicative of a ligand for the receptor or receptor expressing neuron. In one embodiment, positives having a desired functional activity are used to further define the structural descriptors along with previously known activating odorants.
The disclosure also provides a method of computationally screening a vast number of compounds to predict compounds which cause a specific behavior (attraction, repellency, mating, aggression, or oviposition), wherein an compound or set of known compounds causing a specific behavior are used as a training set for selecting optimized molecular descriptors, which can subsequently be used to screen a large collection of untested odorants computationally to identify compounds that are structurally related to the known behavior modifying compounds, outputting the identified putative behavior modifying compounds to a user and testing the compounds for behavior modification, wherein a change compared to baseline behavior is indicative of a behavior modifying compound. In various embodiments, compounds are volatile odors and either the receptor is an odor receptor expressed by a specific neuron or cell type in a specific invertebrate species or receptor-expressing cells are odor receptor neurons present in a specific species of invertebrate.
In other embodiment, compounds are soluble ligands and either the receptor is a gustatory receptor expressed by a specific neuron or cell type in a specific invertebrate species or receptor-expressing cells are gustatory receptor neurons present in a specific species of invertebrate. In yet other embodiments, the compounds are volatile ligands and either the receptor is a gustatory receptor expressed by a specific neuron or cell type in a specific invertebrate species or receptor-expressing cells are gustatory receptor neurons present in a specific species of invertebrate. In further embodiments, the compounds are volatile odors and either the receptor is an odor receptor expressed by a specific neuron or cell type in a specific vertebrate species or receptor-expressing cells are odor receptor neurons present in a specific species of mammals. In some embodiments, the compounds are soluble ligands of volatile ligands and either the receptor is a gustatory receptor expressed by a specific neuron or cell type in a specific vertebrate species or receptor-expressing cells are gustatory receptor neurons present in a specific species of mammals.
As mentioned above, the methods of the disclosure can be used to screen ligands for a number of different biological molecules including GPCR. Accordingly, in one embodiment, the compounds are soluble or volatile ligands and either the receptor is a GPCR expressed by a specific neuron or cell type in a specific invertebrate or vertebrate species or receptor-expressing cells are GPCR expressing cells present in a specific species of invertebrate or vertebrate.
In yet other embodiment, the compounds are identified by the method of the disclosure and are identified as compounds for ligand gated ion channels. For example, the compounds can be soluble or volatile ligand and either the receptor is a ligand gated ion channel expressed by a specific neuron or cell type in a specific invertebrate or vertebrate species or receptor-expressing cells are ligand gated ion channel expressing cells present in a specific species of invertebrate or vertebrate.
The disclosure provides a method of identifying a ligand for a biological molecule comprising (a) identifying a known ligand or set of known ligands for a biological molecule, or identifying a compound which causes a specific biological activity, (b) identifying a plurality of descriptors for the known ligand or compound, (c) using a Sequential Forward Selection (SFS) descriptor selection algorithm to incrementally create a unique optimized descriptor subsets from the plurality of descriptors for the known ligand or compound, (d) identifying a putative ligand or compound that best-fits the unique optimized descriptor subset, and (e) testing the putative ligand or compound in a biological assay comprising the biological molecule wherein a change in activity of the biological molecule compared to the molecule without the putative ligand is indicative of a ligand the interacts with the biological molecule.
The disclosure utilizes in one embodiment a Sequential Forward Selection (SFS) descriptor selection method to incrementally create unique optimized descriptor subsets for each odor receptor. For example, starting with the combined group of 3424 descriptors from the full sets of Dragon and Cerius2 descriptors, an initial descriptor was selected whose values for the 109 odors showed the greatest correlation with activity for a specific Or. Additional descriptors were incrementally added to the growing optimized descriptor set based on their ability to further increase the Pearson correlation with activity for a specific Or. Each iteration increased the size of the optimized descriptor set for that Or by one. When a round of descriptor selection failed to increase the correlation between compound distance based upon the descriptor sets and those based upon known compound activity, the selection process was halted. As a result, optimized descriptor sets and their sizes are expected to vary across Ors. Additionally, 6 selection method combinations were used to identify the best statistical method for determining descriptor inclusion in the optimized set (FIG. 2).
In order to identify a method to select optimized descriptors for each Or the method was applied to 18 combinations of distance metrics, descriptor sets, and activity thresholds. Distance metrics included Euclidean, Spearman, and Pearson coefficients. Descriptor sets included Dragon, Cerius2, and a combined Dragon/Cerius2 set from which optimized descriptors would be chosen. Two activity threshold methods were compared for each combination. First, the four (>200, >150, >100, and >50 spikes/second) activity cut-offs were used. Second, a cluster based cut-off method was used to determine actives. For this approach a cluster analysis of the 109 odors for each individual Or was used using compound activity to calculate distances between Ors. The resulting activity trees for each Or were inspected, and active compounds were classified by selecting either one or two branches containing the active clusters (FIGS. 3A-3T).
Compounds are then clustered based on differences in activity. Compounds falling below a cut point are classified as active. Cut point locations can be determined manually. For example, each of the 3 distance metrics (FIG. 2) were applied to the 6 descriptor subsets (FIG. 1) to produce 18 unique descriptor based odor relationship sets. Accumulative Percentage of Actives (APoAs) values were calculated from distances between compounds based on each of the 18 methods and compared by AUC values as has been described previously. The highest-scoring selection method and the resulting optimized molecular descriptor set were identified for each Or.
If the optimized descriptor sets are better than the large collections of non-optimized descriptors, then one would find that they are able to cluster known active ligands closer together in chemical space. In order to determine whether the optimized descriptor sets are better at bringing the active compounds closer together in chemical space 4 non-optimized descriptor methods including Dragon, Cerius2, Maximum Common Substructure (MCS), Atom Pair (AP), were compared to a “selected” descriptor set from a published study that was selected for activation of the olfactory system by all 20 Drosophila Ors and across multiple species. The averaged APoA values for each of the 6 descriptor sets (Or-optimized, all Dragon, all Cerius2, Atom-pair, MCS, previous study) were compared for each of the 20 Ors and the Or-optimized descriptor sets provided APoA values far greater than all other methods, across all numbers of nearest neighbours.
FIGS. 6A-6T show analyses of APoA for individual Odor receptors. Plots of the mean APoA values obtained from various Molecular Descriptor methods demonstrates that optimized descriptor subsets generate highest values. Molecular descriptor methods were compared using 109 compounds.
The highest-scoring selection method and the resulting optimized molecular descriptor set were identified for each Or. Selection method 5 followed by 11, which proved to work the best by virtue of having the highest AUC scores when considered at an individual Or level, used the combined Dragon+Cerius2 descriptor set, activity-cluster threshold method, and either Euclidean distance Or Spearman correlation as a similarity metric. Euclidean distance provided the highest AUC values for 18 of the Ors and Spearman for 2.
To better visualize how well each Or-optimized descriptor set grouped active ligands, the compounds can be clustered by distances calculated using the optimized descriptor sets for each Or. For example, the 109 compounds were clustered by distances calculated using the optimized descriptor sets for each Or. As expected from the APoA values, highly active ligands are seen tightly clustered for each Or. There were some differences in the ability to cluster actives with Or7a, Or9a, Or10a, Or22a, Or35a, Or43b, Or47a, Or59b, Or67a, Or85a, Or85b and Or98a providing the best clusters, while Or2a, Or23a, Or43a and Or85f did not provide as tight a clustering as predicted. A correlation can be observed between APoA values and the number of highly active compounds grouped tightly together by descriptors. The simplest interpretation of these results is that the Or-descriptor selection method and resulting optimized descriptor sets are considerably better at clustering activating odors than previously tested sets.
The poorer performance of Or2a, Or23a, Or43a and Or85f was expected since of the 109 odorants that were tested, very few showed any activity. The simplest interpretation is that “true” ligands for these 4 receptors have not been discovered from within the tested panel. However, the few odors that poorly activate each of these 4 receptors do cluster together in chemical space after identification of Or-optimized sets, albeit not as well as the ones with known strong ligands. This indicates that the Or-descriptor selection method was able to identify common features amongst the weakly activating odors and hence cluster them together, suggesting the possibility that stronger ligands may be identified from a larger chemical space using this information. From this point onwards these 4 Ors are referred to as “Semi-orphan” Ors.
Using the principles above, an in silico method of compound identification and clustering was used to characterize potential receptor ligands. Since the Or-optimized descriptors can group highly active compounds tightly together in chemical space for each Or, this method can be used to rank untested compounds according to their distance from known actives. This allowed us to computationally screen a vast area of chemical space of potential volatiles in a very efficient and accurate manner. In total close to 5,000,000 interactions were systematically tested between 20 Ors and >240,000 different putative volatile compounds. This would be entirely unfeasible using current assay technology. With electrophysiology the largest screen so far has tested <3000 different interactions, which is  ̃0.06% the size of the in silico screen. Moreover traditional high-throughput plate-based assays, as used for GPCRs that detect ligands in solution, are not appropriate for odor receptors since volatile ligands are largely (if not completely) absent from soluble plate-based combinatorial chemical libraries available.
A large collection of potential volatile compounds were identified by using criteria from known odors, such as molecular weight>200 and atom types limited to C, O, N, S, and H. Using these criteria over 240,000 compounds were selected from Pubchem and their structures were obtained. The distances in chemical space was then calculated for each of the >240,000 compounds based on the Or-optimized descriptor sets for each of the 20 Ors. In this fashion the unknown compounds were sorted by distances from each of the compounds considered as active from the 109 tested compounds. Euclidean distance or Spearman correlation, depending on which had previously been determined to be optimal for the corresponding Or, was used as similarity measures. Using this system the untested compounds in the 240,000 compound library were ranked according to their closeness to the known active ligands. The top 500 (0.2%) of hits in this large chemical space for each Or is listed below. Since each Or-optimized descriptor set was unique, unknowns were ranked independently for each receptor. Compounds were ranked systematically as actives for each of the 20 Ors using the Or-optimized descriptor sets and similarity measures to computationally rank all 240,000 compounds. These predictions could prove to be extremely valuable, not only do they provide an incredibly rich array of information regarding the coding of information by the peripheral olfactory system, it also provides an extremely large number of putative novel ligands for each of these 20 Or genes in Drosophila. 
The results of the in silico screen are provocative. However in order to verify whether these predictions were meaningful, functional evidence was obtained. In order to validate the success of the in silico predictions the responses of 9 Odor receptors using single-sensillum electrophysiology directly on the Drosophila melanogaster antenna were analyzed. For each Odor receptor several odorants were tested from the top 500 predicted hits. A sampling of  ̃192 novel odorants were tested with  ̃11-21 novel odorants tested for each receptor, which were scattered somewhat randomly within the top 500 predictions for each receptor, providing a relatively unbiased set of chemical structures.
To test identified compounds any number of biological assays can be used to measure ORN activity in the presence of a putative ligand/compounds. For example, to demonstrate the activity of the compounds identified above, a single-unit electrophysiology test was performed on D. melanogaster antenna for each predicted compound, resulting in a quantitative value of activation. For the purpose of testing each of these volatile compounds the compounds were diluted to  ̃10−2 in paraffin oil or distilled water. The 9 Ors tested are expressed in well-defined olfactory receptor neurons (ORNs) housed within the large and small basiconic sensilla (ab1-ab7) on the antenna. A previously identified diagnostic panel of odorants was used to distinguish individual classes of sensilla (ab1-ab7) and therefore identified the sensilla that contained the target Or expressing ORN.
FIGS. 10A-10I show the firing rates of odorants that were not predicted to be actives were tested using single unit electrophysiology. This demonstrates the specificity of the invention. Bars indicate the strength of response (spikes/s). All values have been corrected for spontaneous firing rate. Spontaneous activity of neuron was subtracted. All odorants were tested at a concentration of 10−2. N=3. Error bars=s.e.m.
FIGS. 11 and 12A-12F provide a list of exemplary compounds. Chemical name, a 2-D structural image, and distance measure are listed for each tested compound. All distances are Euclidean and represent the distance between each compound and their closest known active by optimized descriptor values. Known active compounds from the training set are in yellow boxes, predicted compounds that were validated as actives are green, inhibitors are red, and inactive compounds are white.
As can be seen a majority of the predicted actives evoked responses from the target ORNs;  ̃71% evoked either activation (>50 spikes/sec above the spontaneous activity) or inhibition (>50% reduction in spontaneous activity). The success rates for different Ors varied from 100% for Or98a, to 27% for Or49b. Extrapolation of these values to the entire in silico screen suggests that between 500 and 135 novel ligands were identified for each of the 19 Ors.
The data demonstrate that >61% of the predicted compounds elicited >50 spikes per second, and >40% evoked strong responses of >100 spikes per second. In a few instances volatiles were identified that could activate the odor receptors extremely strongly (>250 spikes/sec); e.g. isopropyl acetate (Or59b, ab2A) and prenyl acetate (Or98a, ab7A). (see, e.g., FIG. 14).
The top 500 out of 240,000 compounds are an arbitrarily selected criteria and it is possible that compounds beyond the top 500 may also activate the receptors. Further examples were tested using two receptors Or22a and Or85b to extend the analysis to the top 1000 compounds. An additional 4 compounds were selected that are ranked between 500-1000 in the predictions and tested them using electrophysiology. Approximately 100% of these compounds were ligands, suggesting that the total number of new ligands identified by using the top 500 cut-off is underestimated.
Taken together these results demonstrate that the Or-optimized descriptor set based in silico screening of chemical space is extremely efficient at identifying volatile ligands for odor receptors.
The disclosure provides a chemical informatics method that identifies important structural features shared by activating odors for individual odor receptors or olfactory neurons and utilizes these important features to screen large libraries of compounds in silico for novel ligands. These important structural features can also be used to increase understanding of breadth of tuning for each Or in chemical space and perform reverse chemical ecology in silico.
The examples are illustrative. It will be recognized the use of specific odor receptors in the examples below can be substituted with any biological molecule that is capable or binds to a cognate/ligand. Such ligands can be small or large molecule organic molecules. The tables below are also illustrative. Each molecule in the table can be used independently in formulations, compositions or devices or may be used in combination. To described each and every combination would be redundant to the general descriptions herein and one of skill in the art will recognize that the various individual compositions, the various receptors can be utilized by the methods and compositions of the disclosure.
The following examples are intended to illustrate but not limit the disclosure. While they are typical of those that might be used, other procedures known to those skilled in the art may alternatively be used.
EXAMPLES
Chemical informatics. Maximum Common Substructures, Atom Pairs, Cerius2 (Accelerys), Dragon (Talete) were used to compute distances. Energy minimized 3-D structures for Dragon were generated using Omega2 software (OpenEye). Optimized descriptor subsets were identified based on the correlation between descriptor distances with the distances between compounds based upon activity. The process is iteratively used to search for additional descriptors leading to further increases in correlation and stopped when increase stops.
Actives were classified either by thresholds of (>200, >150, >100, and >50 spikes/second), or using cluster analysis of receptor activity to compounds to select branch with strongest actives. The Accumulative Percentage of Actives (APoA) calculated for each descriptor set individually using a method used previously. The Area Under the Curve (AUC) scores from APoA values for each of the combinations were calculated by approximation of the integral under each plotted APoA line.
For each Odor receptor, the “optimized descriptor set” was used to calculate a distance metric that could be used for rank 240,000 compounds according to their closest distance to each known active compound. Compound distances were converted into a relative percentage distances based on the maximum possible compound distance for each Or individually.
Cluster analysis of Ors. Euclidean distance matrixes were used to create clusters using hierarchical clustering and complete linkage for three cases. The first 20 descriptors selected for each Or were used to create an identity matrix. The top 500 predicted compounds were used to create an identity matrix for all Ors. The responses of each of the Ors to a panel of 109 compounds6 were converted into an Or-by-Or Euclidean distance matrix.
Calculation of descriptors. Commercially available software packages Cerius2 (200 individual descriptors) and Dragon (3224 individual descriptors) from Accelerys and Talete were used to calculate molecular descriptors. Prior to inputting compounds into Dragon, 3-Dimensional structures were predicted for compounds through use of the Omega2 software. Descriptor values were normalized across compounds to standard scores by subtracting the mean value for each descriptor type and dividing by the standard deviation. Molecular descriptors that did not show variation across all compounds were removed. Maximum Common Substructures were determined using an existing algorithm. Atom Pairs were computed from the version implemented in Chemmine®.
Classification of active compounds. In Drosophila actives were classified using two methods. In method one four different thresholds were based on the activation of action potentials by the compounds on the odour receptor (>200, >150, >100, and >50 spikes/second) as done in the electrophysiology study. For each odour receptor, APoA values were calculated using odorants falling within each of the four thresholds. The average APoA values for each threshold were then averaged, providing a relatively unbiased representation for which method best brought active odours closer together. In the second method cluster analysis was performed for the 109 compounds for each receptor based on activity in spikes/sec. Active compounds present in a single branch, or two branches, were selected manually as actives.
In mammals actives were classified through cluster analysis. EC50 values obtained were converted to positive values by subtraction from 0 and used directly as measures of compound activity. Converted values ranged from 0 (inactive) to 7.242 (Strongest Activator). Activating compounds for each receptor were clustered by distances in activity. Active compounds present in a single branch, or two branches, were selected manually as actives.
Determination of optimized Drosophila descriptor subsets. A compound-by-compound activity distance matrix was calculated from activity data available for each of the Ors that have been tested for activity to 109 odours. Separate 3424 compound-by-compound descriptor distance matrices were calculated using values from Dragon and Cerius2. Active compounds for each Or were identified individually through activity thresholds. The correlation between the compound-by-compound activity and compound-by-compound descriptor distance matrices were compared for each actively classified compound, considering their distances to all other compounds. The goal was to identify the descriptor that calculates distance between compounds that most closely correlates with the distances between compounds based upon activity. The descriptor that correlates best is retained and the process iteratively used to search for additional descriptors leading to further increases in correlation. In this manner the size of the optimized descriptor set increases by one in each iteration as the best descriptor set from the previous step is combined with all possible descriptors to find the next best descriptor. This process is halted when all possible descriptor additions in iteration fail to improve the correlation value from the previous step. This whole process is repeated once for each Or resulting in unique descriptor sets that are optimized for each Or.
Determination of optimized mammalian descriptor subsets. Mammalian descriptor set optimization was performed the same as for drosophila. The only difference for mammalian is that actives were classified only by cluster analysis.
Calculation of Accumulative Percentage of Actives (APoA). The accumulative percentage of actives is calculated for each descriptor set individually using a method used previously. The “optimized descriptor set” for a given odour receptor is used to calculate distances (Euclidean or Spearman) between the 109 compounds of known activity and the compounds are ranked according to their distance from each known active, resulting in one set of ranked compound distances for each active. Moving down the list for each of these rankings, ratios are calculated for the number of active compounds observed divided by the total number of compounds inspected, or the APoA. APoA values are averaged across all active compound rankings, creating a single set of mean values representing the APoA for a single Or and descriptor set. Using this approach ApoA mean values are calculated for each of the 24 Odour receptors, separately for each of the descriptor sets used, optimized set, all Dragon, all Cerius2, Atom Pair, Maximum Common Substructure. The Area Under the Curve (AUC) scores from APoA values for each of the combinations were calculated by approximation of the integral under each plotted APoA line.
Ranking untested putative volatile compounds. A large collection of >240,000 untested compound structures were obtained from Pubchem using the following criteria. Compounds had molecular weights between 32 and 200 and were limited to H, C, N, O, or S atom types. Compound structures were converted into 3-Dimensional models using Omega2. Cerius2 and Dragon descriptors were calculated for each compound followed by the standard normalization of values through subtraction of the mean and division by standard deviation. For each Odour receptor, the previously determined “optimized descriptor set” was used to calculate a distance metric that could be used for ranking. The known active compounds for each Or were used individually to rank the set of greater than 240,000 compounds according to their closest distance to each known active compound, resulting in a matrix of dimensions 240,000 by the number of actives for the particular Or. Using this matrix each of the 240,000 compound structures were ranked according to their closest distance to any known active compound.
Clustering Ors by Most Common Descriptors. The first 20 descriptors selected by the optimized descriptor selection algorithm for each Or were used to create an identity matrix. Each row representing an Or and column a specific descriptor. Ors that share common descriptors contain is in the same column. This matrix was then converted into an Or by Or Euclidean distance matrix and clustered using hierarchical clustering and complete linkage.
Clustering compounds by activity of Or. The responses of each of the Ors that had previously been tested against a panel of compounds were converted into an Or-by-Or Euclidean distance matrix. Ors were clustered using hierarchical clustering and complete linkage. Specifically, this was achieved by creating a compound-by-compound distance matrix using the differences in activity between compounds tested on a single Or. Hierarchical clustering using each Or distance matrix and then manually identifying the sub cluster which contained the most compact group of highly active compounds resulted in each Or's actively classified compounds.
Calculation of Pharmacophores. Pharmacophore calculation was performed by Ligand Scout. Tightly clustering validated compounds for each Drosophila Or were aligned by shared pharmacophore features.
Clustering Ors by predicted ligand space. Percentages of overlapping predictions within the top 500 predicted compounds were calculated pair-wise for all Ors. Euclidean distances were calculated from the similarity between Ors
Calculation of Or Tuning Using Pubchem and Collected Datasets. Initially all extreme outliers were manually removed from the dataset for each Or. On average 5.82 compounds were manually removed for each Or resulting in a mean dataset reduction of 0.0024%. Next all compounds whose distance was greater than 3 standard deviations from the strongest activating compound were removed to reduce outliers. Distance-densities were produced for each Or. The large majority of these densities follow a Gaussian distribution with the exception of Or10, which appears bimodal. All remaining compound distances were converted into a relative percentage distances based on the maximum possible compound distance for each Or individually. The numbers of compounds within the top 15 percent of relative distance were plotted on a logarithmic scale for each Or to generate computationally derived tuning curves. The same maximum distance value for each Or was also used to calculate and plot the top 15 percent of collected compound relative distance.
Collected Volatile Compound Library. A subset of 3197 volatile compounds were assembled from acknowledged origins including plants, humans, and a fragrance collection (Sigma flavours & fragrances, 2003 and 2007) that may have additional fruit and floral volatiles.
Calculation of Breadth of Or Tuning Across Datasets. From each of the three datasets (Hallem, Collected, Collected+Pubchem) an Or by Compound binary identity matrix was created. For the Hallem plot all compounds known to activate at least one Or at greater than 50 spikes/sec and any Or for which at least one activating compound was known were considered. Using these criteria the identity matrix was created and filled for each case of Or activation. For both the Collected and Pubchem+Collected datasets the top 500 predicted compounds for each Or for which predictions were made were used to fill binary identity matrices. All matrices were sorted in decreasing order of the percent of either known or predicted cross activation and plotted.
Computational validation of drosophila optimized descriptor sets. A 5-fold cross-validation was performed by dividing the dataset into 5 equal sized partitions containing roughly 22 compounds each. During each run, one of the partitions is selected for testing, and the remaining 4 sets are used for training. The training process is repeated 5 times with each unique odorant set being used as the test set exactly once. For each training iteration a unique set of descriptors was calculated from the training compound set. These descriptors were then used to calculate minimum distances from the test set compounds to the closest active exactly as used to predict ligands in a ligand discovery pipeline. Once test set compounds have been ranked by distance from closest to furthest to a known active in the training set, a receiver operating characteristics (ROC) analysis is used to analyze the performance of the computational ligand prediction approach. This analysis was performed on 12 Ors that were activated strongly by at least five odors (>100 spikes/sec) and very strongly by at least one odor (>150 spikes/sec) and were considered to have sufficient known ligands for this type of validation (Or7a, Or9a, Or10a, Or22a, Or35a, Or43b, Or47a, Or59b, Or67a, Or67c, Or85b, Or98a). A single average ROC curve for all 12 Ors was calculated and plotted (FIG. 9A).
Computational Validation of mammalian OR Compound Clustering. A 5-fold cross-validation was performed by dividing the dataset into 5 equal sized partitions containing 12 compounds each. During each run, one of the partitions is selected for testing, and the remaining 4 sets are used for training. The training process is repeated 5 times with each unique odorant set being used as the test set exactly once. For each training iteration a unique set of descriptors was calculated from the training compound set. These descriptors were then used to calculate minimum distances from the test set compounds to the closest active exactly as used to predict ligands in the ligand discovery pipeline. Once test set compounds have been ranked by distance from closest to furthest to a known active in the training set, a receiver operating characteristics (ROC) analysis is used to analyze the performance of the computational ligand prediction approach. Using ROC one can determine the predictive ability for 7 of the most broadly tuned receptors (Or2W1, MOr271-1, MOr203-1, Or1A1, MOr272-1, MOr139-1, and MOr41-1). To retain as many active compounds for each test set division as possible, the activity threshold was reduced for each of the Ors to the lowest level. All compounds with a recorded activation in the previous study were considered “active”. ROC curve averages for all of the compounds were calculated and plotted (FIGS. 18A-18G).
Or-Ligand Interaction Map. The Or-ligand interaction map was developed using Cytoscape. Each predicted Or-ligand interaction from the top 500 predicted ligands for all of the Ors listed Table 4 were used to calculate the map. All predicted interactions are labelled in grey. In addition all interactions identified in this study, previous study and interactions for ab1A and ab1B from another study were included and labelled in black. All compounds are represented as small black circles and Ors are represented as large coloured circles. Or names are provided on the upper right corner of each Or.
Electrophysiology. Extracellular single-sensillum electrophysiology was performed as before with a few modifications. 50 □l odor at 10−2 dilution in paraffin oil was applied to cotton wool in odor cartridge. Odor stimulus flow=12 ml/second. Due to variability in temporal kinetics of response across various odors, the counting window was shortened to 250 milliseconds from the start of odor stimulus. A diagnostic panel of odorants to distinguish individual classes of sensilla (ab1-ab7) and therefore unequivocally identified the target ORN.
Since the structure of receptor protein complexes is not known odor-receptor interactions were analyzed by applying the similarity property principle, which reasons that structurally similar molecules (e.g. activating odorants) are more likely to have similar properties. To identify a method that describes common structural features shared by receptor actives in a quantitative fashion tractable for computational analysis four types of vastly differing molecular descriptor systems were tested: Cerius2 (Accelrys Software Inc), Dragon (Talete), Maximum-Common-Substructure, and Atom-Pair, to construct a chemical space for 109 odors that had previously been tested against 24 odor receptors from Drosophila melanogaster. These represent virtually all of the Or genes expressed in the Drosophila antenna. The four descriptor methods and associated similarity measures varied in their ability to group actives close together in descriptor space as measured for each Or using Accumulative Percentage of Actives (APoA) and value of Area Under the Curve (AUC).
Individual Ors are tuned to overlapping but distinct subsets of ligands. It was reasoned that cherry-picked subsets of molecular descriptors that are suited to cluster actives for an individual Or may be more effective at defining Or-specific chemical space, rather than the entire descriptor set that likely includes a number of features irrelevant for that Or. Using a Sequential-Forward-Selection method similar to previously used approaches unique optimized descriptor subsets were incrementally created for each Or from an initial set of 3424 Dragon and Cerius2 descriptors, which had performed better than Atom Pair and MCS (FIG. 1). 18 combinations of distance metrics, descriptor sets, and activity thresholds, were tested to identify the optimal selection method for each Or (FIGS. 2 and 21A-21W). Not surprisingly, the composition of the optimized descriptor sets varied greatly for individual Ors. There is an overwhelming preference for 3-D and 2-D descriptors compared to 1-D and 0-D descriptors, which suggests that structural features rather than the chemical properties of odorants are more important for receptor-odor interactions. The Or-optimized descriptor sets were far superior to non-optimized methods, and to a previous method that did not perform receptor-specific optimization (FIGS. 5 and 21A-21W).
Distances calculated by each Or-optimized descriptor set clustered the highly active compounds ( ̃70%) close together (FIGS. 3A-3T and 8A-8T). In a few cases, such as for Or35a and Or98a, not all the highly active compounds are clustered, suggesting the possibility of multiple or flexible binding sites, or imperfect selection of descriptors. Or2a, Or23a, Or43a and Or85f do not have strong actives, however the few weak actives of each of these 4 receptors do cluster together (FIGS. 3A-3T and 8A-8T). Actives of an Or have similar structures and pharmacophore features (FIGS. 3A-3T and 8A-8T).
Since Or-optimized descriptors can group highly active compounds in chemical space, There were used to rank untested compounds according to their distance from known actives. Approximately 4,500,000 odor-receptors interactions were systematically screened in silico, representing 19 Ors and >240,000 putative volatile compounds, a scale >1500 times that achieved in previous electrophysiology studies of odor-receptor interactions. This represents a significant achievement since high-throughput plate-based assays are not appropriate for screening volatile Or ligands, which are largely absent from the soluble combinatorial chemical libraries available for such methods. The top 500 (0.2%) hits from this vast chemical library for each of the 19 Ors were generated a fraction of which are presented in Table 4.
To validate the in silico screen several untested odorants were obtained (192;  ̃11-25/Or) belonging to the top 500 predicted ligands for 9 different Ors (Tables 3 and 4). They were systematically tested with each predicted receptor-odor combination using single-unit electrophysiology to record from the olfactory receptor neurons (ORNs) to which these 9 Ors have been previously mapped in the D. melanogaster antenna (FIGS. 17A-17AK and 18A-18G). A majority of the predicted actives evoked responses from the target ORNs (FIG. 18);  ̃75% evoked either activation (>50 spikes/sec above the spontaneous activity) or inhibition (>50% reduction in spontaneous activity) (FIG. 14). The success rate varied between Ors (27%-100%). A number of predicted actives that do not evoke a response (16/44) are compounds with very low volatility, raising the possibility that they may not be delivered at adequate levels to the ORNs. Taken together the physiological analysis provides the most important validation of the Or-optimized descriptor-based in silico screen of chemical space to identify volatile ligands for Ors. Previous studies have not performed well (<25% success for >50 spikes/sec) in evaluating novel odorants.
Approximately 10% of the predicted compounds showed a strong inhibitory effect (FIGS. 10A-10I, 14, 15A-15C and 16A-16B). Interestingly, inhibitors for 3 receptors, Or22a, Or47a and Or59b, were identified for which there are no previously reported inhibitors. Compounds that inhibit Ors were identified by virtue of structural similarity to Or activators. Thus the approach may provide a high-throughput method to identify putative competitive inhibitors and provide tools to investigate mechanisms of Or inhibition and their consequences in blocking specific behaviors.
Although an increasing number of insect Ors are being decoded using various methods like the Drosophila “empty neuron” system, and heterologous expression in Xenopus oocytes or cells, the process is extremely tedious and expensive. However, information on odor response profiles of single ORNs is available for several species of insects and vertebrates and relatively easy to obtain using single cell recording and/or imaging techniques. In most cases, individual ORNs ensure expression of a single Or gene and the response specificity of an ORN is imparted primarily by this associated Or. One can perform descriptor optimization using the odor response profile of the ORN directly. Or92a and Or42b have not been decoded however their corresponding antennal ORNs (ab1A and ab1B) have been tested with a panel of 47 odors. ORN-optimized descriptor sets (FIG. 12A-12F) that were efficient at clustering actives close together in chemical space were used (FIG. 15A-B). The ORN-optimized descriptor sets for ab1A and ab1B were used to screen the >240,000 library and predicted 500 novel ligands as before (Table 3, 4). Approximately 20 novel compounds were tested for each ORN, which revealed a high degree of success: >68% for ab1A and >94% for ab1B (FIGS. 15A and 15B).
Or82a was intractable to the selection of Or-optimized descriptors because it is activated strongly by a single compound, geranyl acetate, a pheromone-like long-chain hydrocarbon compound. Or82a activity is reminiscent of known insect pheromone receptors, which are often responsive only to single compounds and present an extreme challenge to understanding receptor-odor interactions. To identify novel ligands for the narrowly tuned Or82a, three additional activators of Or82a were identified from approximate predictions made using all 3424 Dragon and Cerius2 descriptors to calculate distances of >240,000 compounds in the library from geranyl acetate (FIGS. 16A-16B). The new set of four activating ligands was used to identify an Or82a-optimized descriptor set, which was successful in clustering the actives close together in chemical space (FIGS. 16A-16B). As described above, ligands were predicted from the library (Table 4), suggesting that this 3-step process can be used to predict novel ligands for narrowly tuned odor receptors, such as pheromone receptors.
The rate of false negative predictions was examined for each Or using electrophysiology to systematically test ligands of each Or against other non-target receptors. Of >640 non-target receptor-odor interactions tested, only 10.8% evoked a response >50 spikes/sec and 4.3% evoked a response >100 spikes/sec. Considering that the Or-optimized descriptor method did not incorporate any additional computational screening to rule out non-target activators, it is quite specific in its predictive ability.
Drosophila Or proteins are considered to be 7-transmembrane proteins that have a non-traditional inside-out membrane orientation, active as heteromeric ligand-gated ion channels with an obligate partner Or83b. Mammalian odor receptors on the other hand are G-protein coupled receptors with a traditional outside-in 7-transmembrane orientation. Mammals have far larger families of odor receptors ( ̃1000 in mice,  ̃350 in humans) and thus pose a greater challenge to examine odor coding. In order to test whether the chemical informatics platform would be as successful with mammalian odor receptors a similar analysis on 33 odor receptors from mouse and 4 odor receptors from humans was performed, for which responses to a panel of 60 odorants have been determined in heterologous cells and >2 actives have been identified.
Optimized descriptor subsets for each OR were selected from an initial set of 3424 Dragon and Cerius2 descriptors as before (Table 5). The ApoA and the AUC values were comparable, if not better, than the Drosophila Ors suggesting that the descriptors were able to efficiently cluster actives together (FIGS. 17A-17AK). Since the experimental tests of predictions for mammalian receptors are beyond the scope of the analysis, a well-established computational approach to validate the in silico predictions was used. ORs with >15 known ligands were selected and for each OR 20% of the compounds (12/60) were excluded as a test set, while the remaining were used as a training set to generate the optimized descriptors. Distances of all 60 compounds from each of the known actives were calculated in chemical space and classified as active based on activity threshold. This operation was repeated five times for each receptor, each trial performed by excluding a different subset of 20% of the compounds. Average Receiver Operating Characteristic (ROC) curves were generated and AUC values were calculated, which show that optimized-descriptors generated using the training sets could accurately identify actives from the test sets (FIGS. 18A-18G).
The OR-optimized descriptors were the used to systematically screen  ̃8,880,000 odor-receptor interactions in silico, representing 33 mouse ORs, 4 human ORs, and >240,000 putative volatile compounds. The top 500 hits for each receptor represent several potential novel ligands for each receptor from various natural plant and animal sources, fragrances and artificial compounds (Table 6).
Since receptor-optimized descriptor sets and the predicted ligand space they define are a function of shared molecular features that a receptor may employ to recognize ligands, it was important to determine how these characteristics correlate with receptor properties, such as their known activity profiles and amino acid sequences. Hierarchical cluster analysis was used to create trees that represent the various receptors based on: shared descriptors selected; known activity-based relationship; degree of overlap of predicted ligands; and amino acid sequence. In Drosophila, the known activity and the predicted cross-activity trees overlap to a lesser extent to each other than they do to the descriptor tree ( ̃67% Ors present in common subgroups). In contrast, a similar analysis for the mammalian dataset reveals a greater degree of common relationships across the known activity, predicted cross-activity and descriptor trees ( ̃77% ORs present in common subgroups). Similarly, the Drosophila Or-phylogenetic tree has sparser subgroup relationships conserved with each of the other trees (<45%), as opposed to the mammalian ORs where the majority of subgroups in the phylogenetic tree (>56%) are conserved across the various trees. This difference may reflect the much greater amino-acid similarity across the mammalian receptors (47%) as compared to Drosophila (23%).
Coding of odors in a large volatile space (>240,000) by a receptor repertoire is virtually impossible to determine experimentally. Based on the Or-optimized descriptor sets tuning curves were computationally derived for the 22 Drosophila Ors and 36 mammalian receptors in this large chemical space. Substantial variation in the width of the predicted tuning curves for the different receptors was demonstrated. The predicted response profiles suggest that the olfactory system can potentially detect tens of thousands of volatile chemicals, many of which the organism may never have encountered in its chemical environment.
To analyze breadth of tuning and coding potential of the antennal repertoire of Drosophila Ors to natural odors, tuning curves were calculated to an assembled set of 3197 volatile compounds from plants, humans, and a fragrance collection. Plant volatiles constituted an overwhelming majority of compounds that are predicted to be ligands for Drosophila Ors, consistent with its chemical ecology. To further analyze odor source representation odors were classified that belong to top 500 prediction lists according to their source, if known and find that Ors are not specialized for odors from a single source.
To study the predicted ensemble activation patterns of odors across all Ors, the across-receptor activation patterns of the collected compounds were analyzed for each receptor listed in Table 4. Surprisingly only a small fraction (<25%) of the collected odors are predicted to activate multiple Ors. Inclusion of all the top 500 predicted actives for each receptor further reduces the proportion of across-receptor activating compounds. Consistent with this prediction it was demonstrated that cross-activation by ligands evaluated in this study (870 receptor-odor interactions for 10 receptor neurons from FIG. 14) is lower than that reported previously using ligands of comparable strength. These data suggest that a significant number of natural odors may in fact be detected by only one or few receptors, particularly at physiologically relevant concentrations. This concept contrasts with the current model of combinatorial coding in which a majority of volatile chemicals, with the exception of pheromones and CO2, are detected by combinations of various odor receptors. One possible explanation for this disparity is that previously tested subsets of odors were typically chosen on the basis of strong responses in electroantennograms and behavior assays, which could bias towards selection of cross activating odors. The observations that complex fruit odors activate fewer Ors than the number activated by single odors at comparable concentrations such as pentyl acetate, hexanol etc. from a typical test panel, and complex stimuli such as apple-cider-vinegar activate no more than 4-6 glomeruli lend support to this notion. The architecture of the olfactory code therefore appears to integrate two different models. On the one hand, most odors are detected by one or few Ors from the repertoire, which may enhance the specificity and efficiency of the olfactory system for detection of a large number of odors. On the other hand, 15-20% of odors are predicted to activate combinations of Ors (up to 50%), which may serve to increase the resolving capacity of the system in discriminating the defining properties of an odor stimulus.
To create a more generalized metric to quantify odorant similarity all Drosophila Or-specific molecular descriptors were concantonated and used to compute a 322-dimensional space. By visualizing the space in 2-dimensions using the two principle components, the map of the >240K chemical library overlaps well with the 3197 collected-compound volatile library, except for high molecular weight specialized flavor structures. The new ligands identified (+) overlap with previously tested compounds, and odorants distribute according to size and functional group (colors and shapes).
A network view of peripheral odor coding in the Drosophila antenna was created by mapping all predicted and tested odor-receptor combinations as has been done previously for mapping drug-target networks. The ability to decode odor receptors in silico offers a powerful approach to study the chemical ecology of an organism by potentially matching most known odors from a specific environmental source to large repertoires of target receptors or ORNs to engender a systems level view of olfactory system activation. Databases of predicted ligands will provide an invaluable tool for further studies of olfactory systems. The search for novel flavor and fragrance compounds for human beings can also be greatly assisted by a rational prioritization using such a cheminformatics approach. An emerging area of research is the identification of odors that can modify host-seeking behavior in insect disease vectors, either by virtue of their ability to inhibit ORNs that detect host-seeking cues, or by activating ORNs that cause avoidance behavior, or by confounding the pheromone detection pathway and cause mating disruption. In silico screens can provide a rational foundation for identification of novel insect repellents and lures that are environmentally safe and can aid in the fight against insect-borne diseases.







TABLE 1





Optimized descriptor sets for each Drosophila Or. Optimized descriptors occurrences, symbol, brief description,


class, and dimensionality are listed. Descriptors are listed in ascending order of when they were selected into the


optimized set. Weights indicate the number of times a descriptor was selected in an optimized descriptor set. A summary


of the total number of descriptors selected for the receptor repertoire is provided as the beginning.







Drosophila Descriptor Lists





Descriptor Class Type Counts for all Ors










3D-MoRSE descriptors
84



GETAWAY descriptors
84



functional group counts
51



2D autocorrelations
49



edge adjacency indices
49



2D binary fingerprints
48



atom-centred fragments
41



WHIM descriptors
40



topological charge indices
26



atomtypes (Cerius2)
25



molecular properties
24



Burden eigenvalues
23



topological descriptors
22



geometrical descriptors
18



2D frequency fingerprints
11



RDF descriptors
8



walk and path counts
6



information indices
6



topological (Cerius2)
5



connectivity indices
5



constitutional descriptors
4



structural (Cerius2)
3



Randic molecular profiles
2



eigenvalue-based indices
0



charge descriptors
0







Dimensionality Counts (Weights Included)










Num zero dimensional descriptors:
7



Num one dimensional descriptors:
140



Num two dimensional descriptors:
250



Num three dimensional descriptors:
236







Origin (Weights Included)










Num Dragon descriptors:
601



Num Cerius2 descriptors:
33







Dimensionality Counts (Weights Excluded)










Num zero dimensional descriptors:
6



Num one dimensional descriptors:
50



Num two dimensional descriptors:
130



Num three dimensional descriptors:
145







Origin (Weights Excluded)










Num unique Dragon descriptors:
315



Num unique Cerius2 descriptors:
17







Number of Descriptors Per Or










Mean (Weights Included):
41.7



Mean (Weights Excluded):
27







Weights










Mean:
1.5



SD:
1.2



Median:
1



Mode:
1
















Descriptor




Dimen-


(#Unique)
Weight
Symbol
Description
Class
sionality





Or2a (18)
1
Mor18p
3D-MoRSE - signal 18/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



1
Mor17e
3D-MoRSE - signal 17/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



1
Mor28u
3D-MoRSE - signal 28/unweighted
3D-MoRSE descriptors
3



1
J3D
3D-Balaban index
geometrical descriptors
3



2
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
SIC2
structural information content (neighborhood symmetry of 2-order)
information indices
2



1
EEig10x
Eigenvalue 10 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2



1
MATS5e
Moran autocorrelation-lag 5/weighted by atomic Sanderson
2D autocorrelations
2





electronegativities



1
F05[C—O]
frequency of C—O at topological distance 05
2D frequency
2






fingerprints



1
HNar
Narumi harmonic topological index
topological descriptors
2



1
MATS8m
Moran autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2



1
G3s
3st component symmetry directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



1
Mor27m
3D-MoRSE - signal 27/weighted by atomic masses
3D-MoRSE descriptors
3



1
B04[C—O]
presence/absence of C—O at topological distance 04
2D binary fingerprints
2



1
H8v
H autocorrelation of lag 8/weighted by atomic van der Waals volumes
GETAWAY descriptors
3



1
Mor10v
3D-MoRSE - signal 10/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



1
Mor18v
3D-MoRSE - signal 18/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



2
R8p+
R maximal autocorrelation of lag 8/weighted by atomic polarizabilities
GETAWAY descriptors
3


Or7a (31)
1
MAXDP
maximal electrotopological positive variation
topological descriptors
2



1
MAXDN
maximal electrotopological negative variation
topological descriptors
2



1
B06[C—C]
presence/absence of C—C at topological distance 06
2D binary fingerprints
2



2
HATS1v
leverage-weighted autocorrelation of lag 1/weighted by atomic van der
GETAWAY descriptors
3





Waals volumes



3
Hy
hydrophilic factor
molecular properties
1



1
S_ssO
S_ssO
atomtypes (Cerius2)
1



1
JGT
global topological charge index
topological charge
2






indices



2
H-051
H attached to alpha-C
atom-centred
1






fragments



2
EEig10d
Eigenvalue 10 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



5
HATS8u
leverage-weighted autocorrelation of lag 8/unweighted
GETAWAY descriptors
3



1
G2s
2st component symmetry directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



2
Mor16u
3D-MoRSE - signal 16/unweighted
3D-MoRSE descriptors
3



4
B02[O—O]
presence/absence of O—O at topological distance 02
2D binary fingerprints
2



1
R5p+
R maximal autocorrelation of lag 5/weighted by atomic polarizabilities
GETAWAY descriptors
3



1
EEig08d
Eigenvalue 08 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
DISPp
d COMMA2 value/weighted by atomic polarizabilities
geometrical descriptors
3



2
C-008
CHR2X
atom-centred
1






fragments



1
R4e+
R maximal autocorrelation of lag 4/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
EEig09d
Eigenvalue 09 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
nArOH
number of aromatic hydroxyls
functional group
1






counts



1
R2m+
R maximal autocorrelation of lag 2/weighted by atomic masses
GETAWAY descriptors
3



1
nRCOOR
number of esters (aliphatic)
functional group
1






counts



1
B02[C—O]
presence/absence of C—O at topological distance 02
2D binary fingerprints
2



1
GATS7m
Geary autocorrelation - lag 7/weighted by atomic masses
2D autocorrelations
2



1
E2s
2nd component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



1
nRCO
number of ketones (aliphatic)
functional group
1






counts



1
Mor03m
3D-MoRSE - signal 03/weighted by atomic masses
3D-MoRSE descriptors
3



1
MATS8m
Moran autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2



1
CIC5
complementary information content (neighborhood symmetry of 5-order)
information indices
2



1
D/Dr06
distance/detour ring index of order 6
topological descriptors
2


Or9a (29)
1
BEHp8
highest eigenvalue n. 8 of Burden matrix/weighted by atomic
Burden eigenvalues
2





polarizabilities



1
BELv1
lowest eigenvalue n. 1 of Burden matrix/weighted by atomic van der
Burden eigenvalues
2





Waals volumes



1
DISPe
d COMMA2 value/weighted by atomic Sanderson electronegativities
geometrical descriptors
3



2
EEig09d
Eigenvalue 09 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



2
BEHp5
highest eigenvalue n. 5 of Burden matrix/weighted by atomic
Burden eigenvalues
2





polarizabilities



1
E2e
2nd component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





Sanderson electronegativities



1
Mor25m
3D-MoRSE - signal 25/weighted by atomic masses
3D-MoRSE descriptors
3



1
B03[C—C]
presence/absence of C—C at topological distance 03
2D binary fingerprints
2



3
B07[C—C]
presence/absence of C—C at topological distance 07
2D binary fingerprints
2



1
B01[C—O]
presence/absence of C—O at topological distance 01
2D binary fingerprints
2



1
Atype_H_49
Number of Hydrogen Type 49
atomtypes (Cerius2)
1



1
Infective-80
Ghose-Viswanadhan-Wendoloski antiinfective-like index at 80%
molecular properties
1



3
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
Mor22m
3D-MoRSE - signal 22/weighted by atomic masses
3D-MoRSE descriptors
3



1
EEig10d
Eigenvalue 10 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
R1u+
R maximal autocorrelation of lag 1/unweighted
GETAWAY descriptors
3



1
GATS7m
Geary autocorrelation - lag 7/weighted by atomic masses
2D autocorrelations
2



1
MATS4v
Moran autocorrelation - lag 4/weighted by atomic van der Waals volumes
2D autocorrelations
2



1
R4e+
R maximal autocorrelation of lag 4/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
G3p
3st component symmetry directional WHIM index/weighted by atomic
WHIM descriptors
3





polarizabilities



1
Hy
hydrophilic factor
molecular properties
1



1
S_dssC
S_dssC
atomtypes (Cerius2)
1



1
nRCHO
number of aldehydes (aliphatic)
functional group
1






counts



1
B08[C—C]
presence/absence of C—C at topological distance 08
2D binary fingerprints
2



1
R2m
R autocorrelation of lag 2/weighted by atomic masses
GETAWAY descriptors
3



1
HATS5e
leverage-weighted autocorrelation of lag 5/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
D/Dr06
distance/detour ring index of order 6
topological descriptors
2



1
RDF030m
Radial Distribution Function - 3.0/weighted by atomic masses
RDF descriptors
3



2
Jhetv
Balaban-type index from van der Waals weighted distance matrix
topological descriptors
2


Or10a
3
S_dO
S_dO
atomtypes (Cerius2)
1


(11)
1
BEHm7
highest eigenvalue n. 7 of Burden matrix/weighted by atomic masses
Burden eigenvalues
2



1
E2u
2nd component accessibility directional WHIM index/unweighted
WHIM descriptors
3



1
HATS8m
leverage-weighted autocorrelation of lag 8/weighted by atomic masses
GETAWAY descriptors
3



1
BELe4
lowest eigenvalue n. 4 of Burden matrix/weighted by atomic Sanderson
Burden eigenvalues
2





electronegativities



1
Mor25e
3D-MoRSE - signal 25/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



1
B08[C—C]
presence/absence of C—C at topological distance 08
2D binary fingerprints
2



1
JGI3
mean topological charge index of order3
topological charge
2






indices



1
ESpm03u
Spectral moment 03 from edge adj. matrix
edge adjacency indices
2



1
nR = Ct
number of aliphatic tertiary C(sp2)
functional group
1






counts



2
E2e
2nd component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





Sanderson electronegativities


Or19a
1
Mor31p
3D-MoRSE - signal 31/weighted by atomic polarizabilities
3D-MoRSE descriptors
3


(25)
1
H2m
H autocorrelation of lag 2/weighted by atomic masses
GETAWAY descriptors
3



1
L1m
1st component size directional WHIM index/weighted by atomic masses
WHIM descriptors
3



1
R1m+
R maximal autocorrelation of lag 1/weighted by atomic masses
GETAWAY descriptors
3



1
Mor27u
3D-MoRSE - signal 27/unweighted
3D-MoRSE descriptors
3



1
HATS6u
leverage-weighted autocorrelation of lag 6/unweighted
GETAWAY descriptors
3



3
GGI7
topological charge index of order 7
topological charge
2






indices



1
Gs
G total symmetry index/weighted by atomic electrotopological states
WHIM descriptors
3



1
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
H-049
H attached to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp)
atom-centred
1






fragments



1
piPC08
molecular multiple path count of order 08
walk and path counts
2



2
R7u+
R maximal autocorrelation of lag 7/unweighted
GETAWAY descriptors
3



2
G3s
3st component symmetry directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



1
R4m+
R maximal autocorrelation of lag 4/weighted by atomic masses
GETAWAY descriptors
3



1
MATS7p
Moran autocorrelation - lag 7/weighted by atomic polarizabilities
2D autocorrelations
2



1
R6u+
R maximal autocorrelation of lag 6/unweighted
GETAWAY descriptors
3



1
Hy
hydrophilic factor
molecular properties
1



1
ARR
aromatic ratio
constitutional
0






descriptors



1
BEHp7
highest eigenvalue n. 7 of Burden matrix/weighted by atomic
Burden eigenvalues
2





polarizabilities



1
RDF050v
Radial Distribution Function-5.0/weighted by atomic van der Waals
RDF descriptors
3





volumes



1
C-005
CH3X
atom-centred
1






fragments



1
nRCHO
number of aldehydes (aliphatic)
functional group
1






counts



1
nRCOOH
number of carboxylic acids (aliphatic)
functional group
1






counts



1
R5m+
R maximal autocorrelation of lag 5/weighted by atomic masses
GETAWAY descriptors
3



2
C-002
CH2R2
atom-centred
1






fragments


Or22a
1
Mor29v
3D-MoRSE - signal 29/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3


(43)
1
MAXDN
maximal electrotopological negative variation
topological descriptors
2



1
piPC04
molecular multiple path count of order 04
walk and path counts
2



1
Mor10e
3D-MoRSE - signal 10/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



1
Mor27m
3D-MoRSE - signal 27/weighted by atomic masses
3D-MoRSE descriptors
3



1
R7p+
R maximal autocorrelation of lag 7/weighted by atomic polarizabilities
GETAWAY descriptors
3



1
S_sCH3
S_sCH3
atomtypes (Cerius2)
1



2
EEig12r
Eigenvalue 12 from edge adj. matrix weighted by resonance integrals
edge adjacency indices
2



1
nRCOOR
number of esters (aliphatic)
functional group
1






counts



4
R6u+
R maximal autocorrelation of lag 6/unweighted
GETAWAY descriptors
3



1
Mor32p
3D-MoRSE - signal 32/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



1
AlogP98
AlogP98 value
structural (Cerius2)
0



4
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
L3s
3rd component size directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



1
R1v+
R maximal autocorrelation of lag 1/weighted by atomic van der Waals
GETAWAY descriptors
3





volumes



2
nHDon
number of donor atoms for H-bonds (N and O)
functional group
1






counts



2
B10[C—C]
presence/absence of C—C at topological distance 10
2D binary fingerprints
2



1
Mor18m
3D-MoRSE - signal 18/weighted by atomic masses
3D-MoRSE descriptors
3



1
B04[C—O]
presence/absence of C—O at topological distance 04
2D binary fingerprints
2



2
Jhetp
Balaban-type index from polarizability weighted distance matrix
topological descriptors
2



1
STN
spanning tree number (log)
topological descriptors
2



2
ESpm15u
Spectral moment 15 from edge adj. matrix
edge adjacency indices
2



1
GATS1v
Geary autocorrelation - lag 1/weighted by atomic van der Waals volumes
2D autocorrelations
2



1
F03[O—O]
frequency of O—O at topological distance 03
2D frequency
2






fingerprints



1
GATS8m
Geary autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2



2
HATS5e
leverage-weighted autocorrelation of lag 5/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
DISPv
d COMMA2 value/weighted by atomic van der Waals volumes
geometrical descriptors
3



1
R3v+
R maximal autocorrelation of lag 3/weighted by atomic van der Waals
GETAWAY descriptors
3





volumes



1
E2e
2nd component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





Sanderson electronegativities



1
Mor32u
3D-MoRSE - signal 32/unweighted
3D-MoRSE descriptors
3



2
B02[O—O]
presence/absence of O—O at topological distance 02
2D binary fingerprints
2



1
G3e
3st component symmetry directional WHIM index/weighted by atomic
WHIM descriptors
3





Sanderson electronegativities



1
nCrs
number of ring secondary C(sp3)
functional group
1






counts



2
HOMT
HOMA total
geometrical descriptors
3



1
B05[C-C]
presence/absence of C-C at topological distance 05
2D binary fingerprints
2



1
MATS7m
Moran autocorrelation - lag 7/weighted by atomic masses
2D autocorrelations
2



1
RDF030m
Radial Distribution Function-3.0/weighted by atomic masses
RDF descriptors
3



1
EEig12x
Eigenvalue 12 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2



1
R1m+
R maximal autocorrelation of lag 1/weighted by atomic masses
GETAWAY descriptors
3



1
MATS4p
Moran autocorrelation - lag 4/weighted by atomic polarizabilities
2D autocorrelations
2



1
B09[C—O]
presence/absence of C—O at topological distance 09
2D binary fingerprints
2



1
Mor15p
3D-MoRSE - signal 15/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



2
S_sOH
S_sOH
atomtypes (Cerius2)
1


Or23a
1
ATS3p
Broto-Moreau autocorrelation of a topological structure - lag 3/weighted
2D autocorrelations
2


(37)


by atomic polarizabilities



2
O-056
alcohol
atom-centred
1






fragments



1
J3D
3D-Balaban index
geometrical descriptors
3



1
BELm5
lowest eigenvalue n. 5 of Burden matrix/weighted by atomic masses
Burden eigenvalues
2



1
TPSA(Tot)
topological polar surface area using N, O, S, P polar contributions
molecular properties
1



1
B08[C—O]
presence/absence of C—O at topological distance 08
2D binary fingerprints
2



2
Mor27v
3D-MoRSE - signal 27/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



2
R6u+
R maximal autocorrelation of lag 6/unweighted
GETAWAY descriptors
3



1
DISPe
d COMMA2 value/weighted by atomic Sanderson electronegativities
geometrical descriptors
3



1
ESpm12d
Spectral moment 12 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
Mor17m
3D-MoRSE - signal 17/weighted by atomic masses
3D-MoRSE descriptors
3



2
EEig09d
Eigenvalue 09 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
Hy
hydrophilic factor
molecular properties
1



2
GATS3e
Geary autocorrelation - lag 3/weighted by atomic Sanderson
2D autocorrelations
2





electronegativities



1
GATS8m
Geary autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2



1
R4e+
R maximal autocorrelation of lag 4/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
Mor18m
3D-MoRSE - signal 18/weighted by atomic masses
3D-MoRSE descriptors
3



2
nRCOOH
number of carboxylic acids (aliphatic)
functional group
1






counts



1
S_sOH
S_sOH
atomtypes (Cerius2)
1



1
E3m
3rd component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





masses



1
G3s
3st component symmetry directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



2
BELm6
lowest eigenvalue n. 6 of Burden matrix/weighted by atomic masses
Burden eigenvalues
2



1
GATS1m
Geary autocorrelation - lag 1/weighted by atomic masses
2D autocorrelations
2



2
EEig08d
Eigenvalue 08 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
F05[C—O]
frequency of C—O at topological distance 05
2D frequency
2






fingerprints



2
nHDon
number of donor atoms for H-bonds (N and O)
functional group
1






counts



1
EEig10d
Eigenvalue 10 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
R5p+
R maximal autocorrelation of lag 5/weighted by atomic polarizabilities
GETAWAY descriptors
3



1
BIC
BIC
topological (Cerius2)
2



2
Infective-80
Ghose-Viswanadhan-Wendoloski antiinfective-like index at 80%
molecular properties
1



1
GATS4p
Geary autocorrelation - lag 4/weighted by atomic polarizabilities
2D autocorrelations
2



1
DISPp
d COMMA2 value/weighted by atomic polarizabilities
geometrical descriptors
3



1
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
Atype_H_49
Number of Hydrogen Type 49
atomtypes (Cerius2)
1



1
GATS5m
Geary autocorrelation - lag 5/weighted by atomic masses
2D autocorrelations
2



1
B02[O—O]
presence/absence of O—O at topological distance 02
2D binary fingerprints
2



2
JGI5
mean topological charge index of order5
topological charge
2






indices


Or33b
6
O-057
phenol/enol/carboxyl OH
atom-centred
1


(32)



fragments



2
EEig08x
Eigenvalue 08 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2



1
DISPv
d COMMA2 value/weighted by atomic van der Waals volumes
geometrical descriptors
3



1
TPSA(NO)
topological polar surface area using N, O polar contributions
molecular properties
1



5
B06[C—C]
presence/absence of C—C at topological distance 06
2D binary fingerprints
2



4
Atype_H_49
Number of Hydrogen Type 49
atomtypes (Cerius2)
1



2
R3v+
R maximal autocorrelation of lag 3/weighted by atomic van der Waals
GETAWAY descriptors
3





volumes



1
G1e
1st component symmetry directional WHIM index/weighted by atomic
WHIM descriptors
3





Sanderson electronegativities



1
R2m+
R maximal autocorrelation of lag 2/weighted by atomic masses
GETAWAY descriptors
3



4
B05[C—O]
presence/absence of C—O at topological distance 05
2D binary fingerprints
2



1
C-006
CH2RX
atom-centred
1






fragments



2
TPSA(Tot)
topological polar surface area using N, O, S, P polar contributions
molecular properties
1



1
L/Bw
length-to-breadth ratio by WHIM
geometrical descriptors
3



1
EEig08d
Eigenvalue 08 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



3
F04[C—O]
frequency of C—O at topological distance 04
2D frequency
2






fingerprints



1
BEHv5
highest eigenvalue n. 5 of Burden matrix/weighted by atomic van der
Burden eigenvalues
2





Waals volumes



1
Mor30p
3D-MoRSE - signal 30/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



1
nArCO
number of ketones (aromatic)
functional group
1






counts



1
nRCO
number of ketones (aliphatic)
functional group
1






counts



1
R1p+
R maximal autocorrelation of lag 1/weighted by atomic polarizabilities
GETAWAY descriptors
3



1
MATS4p
Moran autocorrelation - lag 4/weighted by atomic polarizabilities
2D autocorrelations
2



1
nN
number of Nitrogen atoms
constitutional
0






descriptors



1
B07[C—C]
presence/absence of C—C at topological distance 07
2D binary fingerprints
2



2
JGI4
mean topological charge index of order4
topological charge
2






indices



1
nRCOOH
number of carboxylic acids (aliphatic)
functional group
1






counts



1
nCconj
number of non-aromatic conjugated C(sp2)
functional group
1






counts



1
C-005
CH3X
atom-centred
1






fragments



1
JGI3
mean topological charge index of order3
topological charge
2






indices



1
HATS3p
leverage-weighted autocorrelation of lag 3/weighted by atomic
GETAWAY descriptors
3





polarizabilities



1
HATS8u
leverage-weighted autocorrelation of lag 8/unweighted
GETAWAY descriptors
3



1
E2u
2nd component accessibility directional WHIM index/unweighted
WHIM descriptors
3



2
H-051
H attached to alpha-C
atom-centred
1






fragments


Or35a
1
ATS4e
Broto-Moreau autocorrelation of a topological structure - lag 4/weighted
2D autocorrelations
2


(51)


by atomic Sanderson electronegativities



2
TPSA(NO)
topological polar surface area using N, O polar contributions
molecular properties
1



1
Mor27p
3D-MoRSE - signal 27/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



8
R6p+
R maximal autocorrelation of lag 6/weighted by atomic polarizabilities
GETAWAY descriptors
3



6
nRCOOH
number of carboxylic acids (aliphatic)
functional group
1






counts



3
EEig10d
Eigenvalue 10 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



2
Gs
G total symmetry index/weighted by atomic electrotopological states
WHIM descriptors
3



9
JGI2
mean topological charge index of order2
topological charge
2






indices



3
EEig12r
Eigenvalue 12 from edge adj. matrix weighted by resonance integrals
edge adjacency indices
2



7
R4e+
R maximal autocorrelation of lag 4/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



7
Mor28e
3D-MoRSE - signal 28/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



5
MATS7p
Moran autocorrelation - lag 7/weighted by atomic polarizabilities
2D autocorrelations
2



2
L3s
3rd component size directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



6
Mor25v
3D-MoRSE - signal 25/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



4
Mor30e
3D-MoRSE - signal 30/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



5
HATS8u
leverage-weighted autocorrelation of lag 8/unweighted
GETAWAY descriptors
3



7
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



3
HATS5m
leverage-weighted autocorrelation of lag 5/weighted by atomic masses
GETAWAY descriptors
3



3
Jhetp
Balaban-type index from polarizability weighted distance matrix
topological descriptors
2



4
JGI8
mean topological charge index of order8
topological charge
2






indices



3
Mor04m
3D-MoRSE - signal 04/weighted by atomic masses
3D-MoRSE descriptors
3



1
S_dssC
S_dssC
atomtypes (Cerius2)
1



2
E1m
1st component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





masses



2
nHDon
number of donor atoms for H-bonds (N and O)
functional group
1






counts



2
RDF135u
Radial Distribution Function-13.5/unweighted
RDF descriptors
3



2
D/Dr06
distance/detour ring index of order 6
topological descriptors
2



3
E2s
2nd component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



2
EEig10r
Eigenvalue 10 from edge adj. matrix weighted by resonance integrals
edge adjacency indices
2



1
G2s
2st component symmetry directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



3
GATS3p
Geary autocorrelation - lag 3/weighted by atomic polarizabilities
2D autocorrelations
2



2
GGI1
topological charge index of order 1
topological charge
2






indices



2
Atype_C_18
Number of Carbon Type 18
atomtypes (Cerius2)
1



1
nRCO
number of ketones (aliphatic)
functional group
1






counts



1
C-005
CH3X
atom-centred
1






fragments



1
Mor27u
3D-MoRSE - signal 27/unweighted
3D-MoRSE descriptors
3



2
F08[C—O]
frequency of C—O at topological distance 08
2D frequency
2






fingerprints



3
G3s
3st component symmetry directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



3
SIC5
structural information content (neighborhood symmetry of 5-order)
information indices
2



1
G(N . . . N)
sum of geometrical distances between N . . . N
geometrical descriptors
3



2
nR = Ct
number of aliphatic tertiary C(sp2)
functional group
1






counts



2
E3m
3rd component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





masses



1
nArCOOR
number of esters (aromatic)
functional group
1






counts



1
HATS6m
leverage-weighted autocorrelation of lag 6/weighted by atomic masses
GETAWAY descriptors
3



1
nArCO
number of ketones (aromatic)
functional group
1






counts



1
Jhete
Balaban-type index from electronegativity weighted distance matrix
topological descriptors
2



1
G(O . . . O)
sum of geometrical distances between O . . . O
geometrical descriptors
3



1
nCt
number of total tertiary C(sp3)
functional group
1






counts



1
H-051
H attached to alpha-C
atom-centred
1






fragments



1
nN
number of Nitrogen atoms
constitutional
0






descriptors



1
P2s
2nd component shape directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



1
C-025
R—CR—R
atom-centred
1






fragments


Or42b
14
R3m+
R autocorrelation of lag 3/weighted by atomic masses
GETAWAY descriptors
3


(ab1B)
1
HATS3m
leverage-weighted autocorrelation of lag 3/weighted by atomic masses
GETAWAY descriptors
3


(13)
1
S_dO
S_dO
atomtypes (Cerius2)
1



4
Mor15m
3D-MoRSE - signal 15/weighted by atomic masses
3D-MoRSE descriptors
3



2
nDB
number of double bonds
constitutional
0






descriptors



4
nRCO
number of ketones (aliphatic)
functional group
1






counts



1
EEig08d
Eigenvalue 08 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



3
nROH
number of hydroxyl groups
functional group
1






counts



2
Ks
K global shape index/weighted by atomic electrotopological states
WHIM descriptors
3



2
B07[C—C]
presence/absence of C—C at topological distance 07
2D binary fingerprints
2



2
E3v
3rd component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





van der Waals volumes



1
P2s
2nd component shape directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



1
R2u+
R autocorrelation of lag 2/unweighted
GETAWAY descriptors
3



1
ESpm15u
Spectral moment 15 from edge adj. matrix
edge adjacency indices
2



1
Mor27e
3D-MoRSE - signal 27/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



1
nArCO
number of ketones (aromatic)
functional group
1






counts



1
B01[C—N]
presence/absence of C—N at topological distance 01
2D binary fingerprints
2



1
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
HATS0p
leverage-weighted autocorrelation of lag 0/weighted by atomic
GETAWAY descriptors
3





polarizabilities



1
EEig08r
Eigenvalue 08 from edge adj. matrix weighted by resonance integrals
edge adjacency indices
2



1
nR-Cs
number of aliphatic secondary C(sp2)
functional group
1






counts



1
R4m+
R autocorrelation of lag 4/weighted by atomic masses
GETAWAY descriptors
3


Or43a
2
O-056
alcohol
atom-centred
1


(27)



fragments



1
BELm5
lowest eigenvalue n. 5 of Burden matrix/weighted by atomic masses
Burden eigenvalues
2



1
B07[C—O]
presence/absence of C—O at topological distance 07
2D binary fingerprints
2



1
R5e
R autocorrelation of lag 5/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
TPSA(Tot)
topological polar surface area using N,O,S,P polar contributions
molecular properties
1



1
R6e+
R maximal autocorrelation of lag 6/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



2
JGI7
mean topological charge index of order7
topological charge
2






indices



3
B04[C—C]
presence/absence of C—C at topological distance 04
2D binary fingerprints
2



1
EEig10d
Eigenvalue 10 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



5
B02[O—O]
presence/absence of O—O at topological distance 02
2D binary fingerprints
2



3
Mor13m
3D-MoRSE - signal 13/weighted by atomic masses
3D-MoRSE descriptors
3



3
nHDon
number of donor atoms for H-bonds (N and O)
functional group
1






counts



1
Mor21m
3D-MoRSE - signal 21/weighted by atomic masses
3D-MoRSE descriptors
3



1
JX
JX
topological (Cerius2)
2



1
R1m+
R maximal autocorrelation of lag 1/weighted by atomic masses
GETAWAY descriptors
3



2
GATS7m
Geary autocorrelation - lag 7/weighted by atomic masses
2D autocorrelations
2



1
BELm6
lowest eigenvalue n. 6 of Burden matrix/weighted by atomic masses
Burden eigenvalues
2



1
E3m
3rd component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





masses



2
MATS3e
Moran autocorrelation - lag 3/weighted by atomic Sanderson
2D autocorrelations
2





electronegativities



1
F04[C—O]
frequency of C—O at topological distance 04
2D frequency
2






fingerprints



1
nRCHO
number of aldehydes (aliphatic)
functional group
1






counts



1
Infective-80
Ghose-Viswanadhan-Wendoloski antiinfective-like index at 80%
molecular properties
1



1
EEig09x
Eigenvalue 09 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2



1
GATS1m
Geary autocorrelation - lag 1/weighted by atomic masses
2D autocorrelations
2



1
CIC2
complementary information content (neighborhood symmetry of 2-order)
information indices
2



1
EEig01d
Eigenvalue 01 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
HATS6u
leverage-weighted autocorrelation of lag 6/unweighted
GETAWAY descriptors
3


Or43b
1
EEig04x
Eigenvalue 04 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2


(29)
1
BEHv4
highest eigenvalue n. 4 of Burden matrix/weighted by atomic van der
Burden eigenvalues
2





Waals volumes



1
Mor25e
3D-MoRSE - signal 25/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



2
EEig09d
Eigenvalue 09 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
E1p
1st component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





polarizabilities



1
BEHe8
highest eigenvalue n. 8 of Burden matrix/weighted by atomic Sanderson
Burden eigenvalues
2





electronegativities



1
R1m+
R maximal autocorrelation of lag 1/weighted by atomic masses
GETAWAY descriptors
3



2
B07[C—C]
presence/absence of C—C at topological distance 07
2D binary fingerprints
2



1
MAXDN
maximal electrotopological negative variation
topological descriptors
2



1
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
Infective-80
Ghose-Viswanadhan-Wendoloski antiinfective-like index at 80%
molecular properties
1



3
B04[CvC]
presence/absence of C—C at topological distance 04
2D binary fingerprints
2



1
MATS5e
Moran autocorrelation - lag 5/weighted by atomic Sanderson
2D autocorrelations
2





electronegativities



1
Mor24v
3D-MoRSE - signal 24/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



1
Mor25v
3D-MoRSE - signal 25/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



1
BEHp4
highest eigenvalue n. 4 of Burden matrix/weighted by atomic
Burden eigenvalues
2





polarizabilities



1
S_sCH3
S_sCH3
atomtypes (Cerius2)
1



1
HATS3p
leverage-weighted autocorrelation of lag 3/weighted by atomic
GETAWAY descriptors
3





polarizabilities



1
H7m
H autocorrelation of lag 7/weighted by atomic masses
GETAWAY descriptors
3



1
JGI7
mean topological charge index of order7
topological charge
2






indices



1
STN
spanning tree number (log)
topological descriptors
2



1
nRCOOH
number of carboxylic acids (aliphatic)
functional group
1






counts



1
MATS6m
Moran autocorrelation - lag 6/weighted by atomic masses
2D autocorrelations
2



1
HATS1u
leverage-weighted autocorrelation of lag 1/unweighted
GETAWAY descriptors
3



1
EEig10d
Eigenvalue 10 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
Atype_H_49
Number of Hydrogen Type 49
atomtypes (Cerius2)
1



1
EEig08d
Eigenvalue 08 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
nCrs
number of ring secondary C(sp3)
functional group
1






counts



2
H-047
H attached to C1(sp3)/C0(sp2)
atom-centred
1






fragments


Or47a
1
piPC04
molecular multiple path count of order 04
walk and path counts
2


(21)
2
DISPm
d COMMA2 value/weighted by atomic masses
geometrical descriptors
3



1
R7e+
R maximal autocorrelation of lag 7/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
Mor10p
3D-MoRSE - signal 10/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



1
Mor20u
3D-MoRSE - signal 20/unweighted
3D-MoRSE descriptors
3



1
IC1
information content index (neighborhood symmetry of 1-order)
information indices
2



1
nRCOOH
number of carboxylic acids (aliphatic)
functional group
1






counts



1
EEig01d
Eigenvalue 01 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



2
Infective-80
Ghose-Viswanadhan-Wendoloski antiinfective-like index at 80%
molecular properties
1



1
MATS4m
Moran autocorrelation - lag 4/weighted-by atomic masses
2D autocorrelations
2



1
GATS5p
Geary autocorrelation - lag 5/weighted by atomic polarizabilities
2D autocorrelations
2



1
PW4
path/walk 4-Randic shape index
topological descriptors
2



1
Mor32p
3D-MoRSE - signal 32/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



1
Mor09e
3D-MoRSE - signal 09/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



1
TPSA(NO)
topological polar surface area using N, O polar contributions
molecular properties
1



1
B04[C—C]
presence/absence of C—C at topological distance 04
2D binary fingerprints
2



1
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
Atype_H_49
Number of Hydrogen Type 49
atomtypes (Cerius2)
1



1
ESpm01d
Spectral moment 01 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
EEig10d
Eigenvalue 10 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
P2m
2nd component shape directional WHIM index/weighted by atomic masses
WHIM descriptors
3



2
Mor06e
3D-MoRSE - signal 06/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3


Or47b
3
EEig02d
Eigenvalue 02 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2


(14)
5
ESpm03d
Spectral moment 03 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
nHBonds
number of intramolecular H-bonds (with N, O, F)
functional group
1






counts



4
X5A
average connectivity index chi-5
connectivity indices
2



1
EEig08x
Eigenvalue 08 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2



1
C-006
CH2RX
atom-centred
1






fragments



1
nRCHO
number of aldehydes (aliphatic)
functional group
1






counts



2
nRCOOR
number of esters (aliphatic)
functional group
1






counts



1
nRCOOH
number of carboxylic acids (aliphatic)
functional group
1






counts



1
EEig08d
Eigenvalue 08 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
X4Av
average valence connectivity index chi-4
connectivity indices
2



1
GATS6m
Geary autocorrelation - lag 6/weighted by atomic masses
2D autocorrelations
2



1
EEig07r
Eigenvalue 07 from edge adj. matrix weighted by resonance integrals
edge adjacency indices
2



1
R2m
R autocorrelation of lag 2/weighted by atomic masses
GETAWAY descriptors
3


Or49b
2
nCb-
number of substituted benzene C(sp2)
functional group
1


(37)



counts



1
BEHm6
highest eigenvalue n. 6 of Burden matrix/weighted by atomic masses
Burden eigenvalues
2



2
F04[C—O]
frequency of C—O at topological distance 04
2D frequency
2






fingerprints



1
D/Dr06
distance/detour ring index of order 6
topological descriptors
2



1
BEHp6
highest eigenvalue n. 6 of Burden matrix/weighted by atomic
Burden eigenvalues
2





polarizabilities



3
H-047
H attached to C1(sp3)/C0(sp2)
atom-centred
1






fragments



1
GATS1m
Geary autocorrelation - lag 1/weighted by atomic masses
2D autocorrelations
2



3
HATS8p
leverage-weighted autocorrelation of lag 8/weighted by atomic
GETAWAY descriptors
3





polarizabilities



2
ISH
standardized information content on the leverage equality
GETAWAY descriptors
3



1
Mor16e
3D-MoRSE - signal 16/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



1
JGI5
mean topological charge index of order5
topological charge
2






indices



1
R8e+
R maximal autocorrelation of lag 8/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
Mor25e
3D-MoRSE - signal 25/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



2
EEig10d
Eigenvalue 10 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
Mor16p
3D-MoRSE - signal 16/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



1
JGI4
mean topological charge index of order4
topological charge
2






indices



1
MATS3p
Moran autocorrelation - lag 3/weighted by atomic polarizabilities
2D autocorrelations
2



3
CIC
CIC
topological (Cerius2)
2



1
P2m
2nd component shape directional WHIM index/weighted by atomic masses
WHIM descriptors
3



1
nHDon
number of donor atoms for H-bonds (N and O)
functional group
1






counts



1
Mor03m
3D-MoRSE - signal 03/weighted by atomic masses
3D-MoRSE descriptors
3



2
JGI7
mean topological charge index of order7
topological charge
2






indices



1
Mor23v
3D-MoRSE - signal 23/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



1
Mor30e
3D-MoRSE - signal 30/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



1
IC
IC
topological (Cerius2)
2



1
Mor21m
3D-MoRSE - signal 21/weighted by atomic masses
3D-MoRSE descriptors
3



1
Mor13m
3D-MoRSE - signal 13/weighted by atomic masses
3D-MoRSE descriptors
3



1
R7v+
R maximal autocorrelation of lag 7/weighted by atomic van der Waals
GETAWAY descriptors
3





volumes



1
piPC07
molecular multiple path count of order 07
walk and path counts
2



1
nArOH
number of aromatic hydroxyls
functional group
1






counts



1
Mor25v
3D-MoRSE - signal 25/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



1
Mor08v
3D-MoRSE - signal 08/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



1
R6e+
R maximal autocorrelation of lag 6/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
EEig06x
Eigenvalue 06 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2



1
C-001
CH3R/CH4
atom-centred
1






fragments



1
Mor07m
3D-MoRSE - signal 07/weighted by atomic masses
3D-MoRSE descriptors
3



1
DISPe
d COMMA2 value/weighted by atomic Sanderson electronegativities
geometrical descriptors
3



1
nR05
number of 5-membered rings
constitutional
0






descriptors



1
Mor07e
3D-MoRSE - signal 07/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



1
EEig09x
Eigenvalue 09 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2



1
B05[C—O]
presence/absence of C—O at topological distance 05
2D binary fingerprints
2



1
X5Av
average valence connectivity index chi-5
connectivity indices
2



1
HATS3p
leverage-weighted autocorrelation of lag 3/weighted by atomic
GETAWAY descriptors
3





polarizabilities



1
R8u+
R maximal autocorrelation of lag 8/unweighted
GETAWAY descriptors
3



1
O-060
Al—O—Ar/Ar—O—Ar/R . . . O . . . R/R—O—C═X
atom-centred
1






fragments



2
B04[C—O]
presence/absence of C—O at topological distance 04
2D binary fingerprints
2


Or59b
1
piPC06
molecular multiple path count of order 06
walk and path counts
2


(23)
1
R3u
R autocorrelation of lag 3/unweighted
GETAWAY descriptors
3



1
S_sCH3
S_sCH3
atomtypes (Cerius2)
1



4
B06[C—C]
presence/absence of C—C at topological distance 06
2D binary fingerprints
2



1
R1e+
R maximal autocorrelation of lag 1/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
ESpm03u
Spectral moment 03 from edge adj. matrix
edge adjacency indices
2



1
EEig10r
Eigenvalue 10 from edge adj. matrix weighted by resonance integrals
edge adjacency indices
2



1
EEig08d
Eigenvalue 08 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
E1u
1st component accessibility directional WHIM index/unweighted
WHIM descriptors
3



1
nCconj
number of non-aromatic conjugated C(sp2)
functional group
1






counts



1
SP13
shape profile no. 13
Randic molecular
3






profiles



2
S_dO
S_dO
atomtypes (Cerius2)
1



2
Atype_H_49
Number of Hydrogen Type 49
atomtypes (Cerius2)
1



1
EEig10d
Eigenvalue 10 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
nHDon
number of donor atoms for H-bonds (N and O)
functional group
1






counts



1
R8u+
R maximal autocorrelation of lag 8/unweighted
GETAWAY descriptors
3



2
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
Mor10v
3D-MoRSE - signal 10/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



1
R5m+
R maximal autocorrelation of lag 5/weighted by atomic masses
GETAWAY descriptors
3



1
Mor09e
3D-MoRSE - signal 09/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



1
nOHp
number of primary alcohols
functional group
1






counts



1
EEig09d
Eigenvalue 09 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
nCrs
number of ring secondary C(sp3)
functional group
1






counts



1
ESpm01d
Spectral moment 01 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2


Or65a
1
F04[O—O]
frequency of O—O at topological distance 04
2D frequency
2


(14)



fingerprints



2
Mor30m
3D-MoRSE - signal 30/weighted by atomic masses
3D-MoRSE descriptors
3



4
Atype_H_51
Number of Hydrogen Type 51
atomtypes (Cerius2)
1



1
EEig08d
Eigenvalue 08 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



2
nArOH
number of aromatic hydroxyls
functional group
1






counts



2
JGI7
mean topological charge index of order7
topological charge
2






indices



1
nHBonds
number of intramolecular H-bonds (with N, O, F)
functional group
1






counts



1
Mor13p
3D-MoRSE - signal 13/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



1
EEig07d
Eigenvalue 07 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
B06[C—O]
presence/absence of C—O at topological distance 06
2D binary fingerprints
2



1
C-008
CHR2X
atom-centred
1






fragments



1
EEig08r
Eigenvalue 08 from edge adj. matrix weighted by resonance integrals
edge adjacency indices
2



1
B01[C—O]
presence/absence of C—O at topological distance 01
2D binary fingerprints
2



2
Mor32e
3D-MoRSE - signal 32/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3


Or67a
2
AlogP98
AlogP98 value
structural (Cerius2)
0


(37)
8
B04[C—O]
presence/absence of C—O at topological distance 04
2D binary fingerprints
2



6
F08[C—O]
frequency of C—O at topological distance 08
2D frequency
2






fingerprints



1
GGI4
topological charge index of order 4
topological charge
2






indices



3
E2u
2nd component accessibility directional WHIM index/unweighted
WHIM descriptors
3



2
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
Mor03v
3D-MoRSE - signal 03/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



4
X5A
average connectivity index chi-5
connectivity indices
2



3
Mor10v
3D-MoRSE - signal 10/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



1
B03[C—O]
presence/absence of C—O at topological distance 03
2D binary fingerprints
2



3
X4A
average connectivity index chi-4
connectivity indices
2



3
nCt
number of total tertiary C(sp3)
functional group
1






counts



1
C-026
R—CX—R
atom-centred
1






fragments
1



3
RDF075m
Radial Distribution Function-7.5/weighted by atomic masses
RDF descriptors
3



2
C-008
CHR2X
atom-centred
1






fragments



2
B03[C—C]
presence/absence of C—C at topological distance 03
2D binary fingerprints
2



1
B01[C—O]
presence/absence of C—O at topological distance 01
2D binary fingerprints
2



1
nRCHO
number of aldehydes (aliphatic)
functional group
1






counts



1
Jhetv
Balaban-type index from van der Waals weighted distance matrix
topological descriptors
2



1
L1s
1st component size directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



1
Hy
hydrophilic factor
molecular properties
1



2
C-003
CHR3
atom-centred
1






fragments



1
GATS7m
Geary autocorrelation - lag 7/weighted by atomic masses
2D autocorrelations
2



1
Mor16e
3D-MoRSE - signal 16/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



1
Mor06u
3D-MoRSE - signal 06/unweighted
3D-MoRSE descriptors
3



1
RDF030m
Radial Distribution Function-3.0/weighted by atomic masses
RDF descriptors
3



1
Atype_C_18
Number of Carbon Type 18
atomtypes (Cerius2)
1



1
F03[O—O]
frequency of O—O at topological distance 03
2D frequency
2






fingerprints



1
nCrs
number of ring secondary C(sp3)
functional group
1






counts



2
nArOH
number of aromatic hydroxyls
functional group
1






counts



1
GATS8m
Geary autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2



1
Jhete
Balaban-type index from electronegativity weighted distance matrix
topological descriptors
2



1
EEig13x
Eigenvalue 13 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2



1
DISPm
d COMMA2 value/weighted by atomic masses
geometrical descriptors
3



1
X3A
average connectivity index chi-3
connectivity indices
2



1
G(N . . . N)
sum of geometrical distances between N . . . N
geometrical descriptors
3



1
Mor32u
3D-MoRSE - signal 32/unweighted
3D-MoRSE descriptors
3


Or67c
1
BEHe8
highest eigenvalue n. 8 of Burden matrix/weighted by atomic Sanderson
Burden eigenvalues
2


(24)


electronegativities



1
O-056
alcohol
atom-centred
1






fragments



1
Mor25m
3D-MoRSE - signal 25/weighted by atomic masses
3D-MoRSE descriptors
3



1
BELv4
lowest eigenvalue n. 4 of Burden matrix/weighted by atomic van der
Burden eigenvalues
2





Waals volumes



3
B07[C—C]
presence/absence of C—C at topological distance 07
2D binary fingerprints
2



1
TPSA(Tot)
topological polar surface area using N, O, S, P polar contributions
molecular properties
1



1
DISPm
d COMMA2 value/weighted by atomic masses
geometrical descriptors
3



4
HATS6u
leverage-weighted autocorrelation of lag 6/unweighted
GETAWAY descriptors
3



2
EEig08d
Eigenvalue 08 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



2
EEig10d
Eigenvalue 10 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
Gs
G total symmetry index/weighted by atomic electrotopological states
WHIM descriptors
3



3
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
B08[C—C]
presence/absence of C—C at topological distance 08
2D binary fingerprints
2



1
R1m+
R maximal autocorrelation of lag 1/weighted by atomic masses
GETAWAY descriptors
3



1
BELm5
lowest eigenvalue n. 5 of Burden matrix/weighted by atomic masses
Burden eigenvalues
2



1
F03[O—O]
frequency of O—O at topological distance 03
2D frequency
2






fingerprints



1
STN
spanning tree number (log)
topological descriptors
2



1
Atype_H_49
Number of Hydrogen Type 49
atomtypes (Cerius2)
1



1
H-051
H attached to alpha-C
atom-centred
1






fragments



1
B01[C—O]
presence/absence of C—O at topological distance 01
2D binary fingerprints
2



1
Infective-80
Ghose-Viswanadhan-Wendoloski antiinfective-like index at 80%
molecular properties
1



1
Hy
hydrophilic factor
molecular properties
1



1
Mor22m
3D-MoRSE - signal 22/weighted by atomic masses
3D-MoRSE descriptors
3



1
JGI7
mean topological charge index of order7
topological charge
2






indices


Or82a
1
GGI9
topological charge index of order 9
topological charge
2


(31)



indices



1
Mor02e
3D-MoRSE - signal 02/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



1
Mor30v
3D-MoRSE - signal 30/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



1
Mor02v
3D-MoRSE - signal 02/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



1
Mor30u
3D-MoRSE - signal 30/unweighted
3D-MoRSE descriptors
3



2
BLTD48
Verhaar model of Daphnia base-line toxicity from MLOGP (mmol/l)
molecular properties
1



2
Mor10v
3D-MoRSE - signal 10/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



2
Atype_H_53
Number of Hydrogen Type 53
atomtypes (Cerius2)
1



1
O-058
═O
atom-centred
1






fragments



1
B02[C—O]
presence/absence of C—O at topological distance 02
2D binary fingerprints
2



2
R5u+
R maximal autocorrelation of lag 5/unweighted
GETAWAY descriptors
3



1
H6e
H autocorrelation of lag 6/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
MATS7p
Moran autocorrelation - lag 7/weighted by atomic polarizabilities
2D autocorrelations
2



1
GATS3p
Geary autocorrelation - lag 3/weighted by atomic polarizabilities
2D autocorrelations
2



1
Mor18m
3D-MoRSE - signal 18/weighted by atomic masses
3D-MoRSE descriptors
3



1
H-051
H attached to alpha-C
atom-centred
1






fragments



2
Mor13p
3D-MoRSE - signal 13/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



1
SIC2
structural information content (neighborhood symmetry of 2-order)
information indices
2



1
Mor32u
3D-MoRSE - signal 32/unweighted
3D-MoRSE descriptors
3



1
Mor10m
3D-MoRSE - signal 10/weighted by atomic masses
3D-MoRSE descriptors
3



1
nR = Cp
number of terminal primary C(sp2)
functional group
1






counts



1
Mor25p
3D-MoRSE - signal 25/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



1
GATS8m
Geary autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2



1
JGI1
mean topological charge index of order 1
topological charge
2






indices



1
E-ADJ-mag
E-ADJ-mag
topological (cerius2)
2



1
EEig11x
Eigenvalue 11 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2



1
B03[O—O]
presence/absence of O—O at topological distance 03
2D binary fingerprints
2



1
Mor30e
3D-MoRSE - signal 30/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



1
Rotlbonds
Number of rotatable bonds
structural (Cerius2)
0



1
EEig09d
Eigenvalue 09 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



2
GATS7m
Geary autocorrelation - lag 7/weighted by atomic masses
2D autocorrelations
2


Or85a
1
EEig04r
Eigenvalue 04 from edge adj. matrix weighted by resonance integrals
edge adjacency indices
2


(15)
2
C-006
CH2RX
atom-centred
1






fragments



3
ATS6e
Broto-Moreau autocorrelation of a topological structure - lag 6/weighted
2D autocorrelations
2





by atomic Sanderson electronegativities



3
JGI5
mean topological charge index of order5
topological charge
2






indices



2
B07[C—C]
presence/absence of C—C at topological distance 07
2D binary fingerprints
2



1
nCp
number of terminal primary C(sp3)
functional group
1






counts



2
DISPm
d COMMA2 value/weighted by atomic masses
geometrical descriptors
3



2
GATS4m
Geary autocorrelation - lag 4/weighted by atomic masses
2D autocorrelations
2



1
Mor25p
3D-MoRSE - signal 25/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



1
nHDon
number of donor atoms for H-bonds (N and O)
functional group
1






counts



1
EEig09d
Eigenvalue 09 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
R2m+
R maximal autocorrelation of lag 2/weighted by atomic masses
GETAWAY descriptors
3



1
JGI4
mean topological charge index of order4
topological charge
2






indices



1
Mor11e
3D-MoRSE - signal 11/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3



2
HATS7m
leverage-weighted autocorrelation of lag 7/weighted by atomic masses
GETAWAY descriptors
3


Or85b
1
piPC05
molecular multiple path count of order 05
walk and path counts
2


(26)
1
BLTF96
Verhaar model of Fish base-line toxicity from MLOGP (mmol/l)
molecular properties
1



2
GATS4p
Geary autocorrelation - lag 4/weighted by atomic polarizabilities
2D autocorrelations
2



1
GGI7
topological charge index of order 7
topological charge
2






indices



3
B05[C—O]
presence/absence of C—O at topological distance 05
2D binary fingerprints
2



2
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
Mor27v
3D-MoRSE - signal 27/weighted by atomic van der Waals volumes
3D-MoRSE descriptors
3



1
HATS4v
leverage-weighted autocorrelation of lag 4/weighted by atomic van der
GETAWAY descriptors
3





Waals volumes



1
Gs
G total symmetry index/weighted by atomic electrotopological states
WHIM descriptors
3



2
Infective-80
Ghose-Viswanadhan-Wendoloski antiinfective-like index at 80%
molecular properties
1



2
R7u+
R maximal autocorrelation of lag 7/unweighted
GETAWAY descriptors
3



2
nCbH
number of unsubstituted benzene C(sp2)
functional group
1






counts



1
B04[C—O]
presence/absence of C—O at topological distance 04
2D binary fingerprints
2



2
JGI7
mean topological charge index of order7
topological charge
2






indices



2
DISPe
d COMMA2 value/weighted by atomic Sanderson electronegativities
geometrical descriptors
3



1
R4p+
R maximal autocorrelation of lag 4/weighted by atomic polarizabilities
GETAWAY descriptors
3



1
EEig12x
Eigenvalue 12 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2



1
B06[C—O]
presence/absence of C—O at topological distance 06
2D binary fingerprints
2



1
MATS5e
Moran autocorrelation - lag 5/weighted by atomic Sanderson
2D autocorrelations
2





electronegativities



1
HATS4m
leverage-weighted autocorrelation of lag 4/weighted by atomic masses
GETAWAY descriptors
3



1
HATS6u
leverage-weighted autocorrelation of lag 6/unweighted
GETAWAY descriptors
3



1
GATS4m
Geary autocorrelation - lag 4/weighted by atomic masses
2D autocorrelations
2



1
F03[O—O]
frequency of O—O at topological distance 03
2D frequency
2






fingerprints



1
H8v
H autocorrelation of lag 8/weighted by atomic van der Waals volumes
GETAWAY descriptors
3



1
EEig09d
Eigenvalue 09 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



2
Mor16e
3D-MoRSE - signal 16/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3


Or85f
1
BEHp8
highest eigenvalue n. 8 of Burden matrix/weighted by atomic
Burden eigenvalues
2


(53)


polarizabilities



5
F05[C—O]
frequency of C—O at topological distance 05
2D frequency
2






fingerprints



4
BELm4
lowest eigenvalue n. 4 of Burden matrix/weighted by atomic masses
Burden eigenvalues
2



1
HATS8m
leverage-weighted autocorrelation of lag 8/weighted by atomic masses
GETAWAY descriptors
3



2
B04[C—O]
presence/absence of C—O at topological distance 04
2D binary fingerprints
2



6
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
RDF030v
Radial Distribution Function-3.0/weighted by atomic van der Waals
RDF descriptors
3





volumes



1
GGI7
topological charge index of order 7
topological charge
2






indices



1
Gs
G total symmetry index/weighted by atomic electrotopological states
WHIM descriptors
3



4
B07[C—C]
presence/absence of C—C at topological distance 07
2D binary fingerprints
2



1
E2e
2nd component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





Sanderson electronegativities



1
MATS2m
Moran autocorrelation - lag 2/weighted by atomic masses
2D autocorrelations
2



2
Mor28u
3D-MoRSE - signal 28/unweighted
3D-MoRSE descriptors
3



3
BEHp5
highest eigenvalue n. 5 of Burden matrix/weighted by atomic
Burden eigenvalues
2





polarizabilities



2
Infective-80
Ghose-Viswanadhan-Wendoloski antiinfective-like index at 80%
molecular properties
1



1
HATS4e
leverage-weighted autocorrelation of lag 4/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



3
JGI6
mean topological charge index of order6
topological charge
2






indices



6
B05[C—O]
presence/absence of C—O at topological distance 05
2D binary fingerprints
2



2
JGI7
mean topological charge index of order7
topological charge
2






indices



2
DISPm
d COMMA2 value/weighted by atomic masses
geometrical descriptors
3



5
RDF030m
Radial Distribution Function-3.0/weighted by atomic masses
RDF descriptors
3



1
R1e+
R maximal autocorrelation of lag 1/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
HATS8p
leverage-weighted autocorrelation of lag 8/weighted by atomic
GETAWAY descriptors
3





polarizabilities



1
Atype_H_49
Number of Hydrogen Type 49
atomtypes (Cerius2)
1



2
Hy
hydrophilic factor
molecular properties
1



1
Jhetp
Balaban-type index from polarizability weighted distance matrix
topological descriptors
2



1
H8v
H autocorrelation of lag 8/weighted by atomic van der Waals volumes
GETAWAY descriptors
3



2
EEig11d
Eigenvalue 11 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
MATS8m
Moran autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2



1
MATS2p
Moran autocorrelation - lag 2/weighted by atomic polarizabilities
2D autocorrelations
2



4
B08[C—C]
presence/absence of C—C at topological distance 08
2D binary fingerprints
2



1
S_sCH3
S_sCH3
atomtypes (Cerius2)
1



2
HATS1e
leverage-weighted autocorrelation of lag 1/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
nCconj
number of non-aromatic conjugated C(sp2)
functional group
1






counts



1
B04[C—C]
presence/absence of C—C at topological distance 04
2D binary fingerprints
2



1
S_aasC
S_aasC
atomtypes (cerius2)
1



1
R8m+
R maximal autocorrelation of lag 8/weighted by atomic masses
GETAWAY descriptors
3



1
nRCOOH
number of carboxylic acids (aliphatic)
fundtional group
1






counts



1
S_sOH
S_sOH
atomtypes (Cerius2)
1



1
BELe3
lowest eigenvalue n. 3 of Burden matrix/weighted by atomic Sanderson
Burden eigenvalues
2





electronegativities



1
GATS8m
Geary autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2



1
BEHp4
highest eigenvalue n. 4 of Burden matrix/weighted by atomic
Burden eigenvalues
2





polarizabilities



2
MATS5e
Moran autocorrelation - lag 5/weighted by atomic Sanderson
2D autocorrelations
2





electronegativities



1
E3s
3rd component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



2
Jhetv
Balaban-type index from van der Waals weighted distance matrix
topological descriptors
2



1
nR = Ct
number of aliphatic tertiary C(sp2)
functional group
1






counts



1
nRCHO
number of aldehydes (aliphatic)
functional group
1






counts



1
HATS8v
leverage-weighted autocorrelation of lag 8/weighted by atomic van der
GETAWAY descriptors
3





Waals volumes



1
Mor28p
3D-MoRSE - signal 28/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



1
C-003
CHR3
atom-centred
1






fragments



1
GATS7m
Geary autocorrelation - lag 7/weighted by atomic masses
2D autocorrelations
2



1
JGI9
mean topological charge index of order9
topological charge
2






indices



1
B03[C—C]
presence/absence of C—C at topological distance 03
2D binary fingerprints
2


Or88a
3
nHBonds
number of intramolecular H-bonds (with N, O, F)
functional group
1


(19)



counts



2
nRCO
number of ketones (aliphatic)
functional group
1






counts



3
GATS6m
Geary autocorrelation - lag 6/weighted by atomic masses
2D autocorrelations
2



2
EEig08x
Eigenvalue 08 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2



1
nFuranes
number of Furanes
functional group
1






counts



1
nArCO
number of ketones (aromatic)
functional group
1






counts



1
ESpm15d
Spectral moment 15 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
C-005
CH3X
atom-centred
1






fragments



1
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



1
L/Bw
length-to-breadth ratio by WHIM
geometrical descriptors
3



1
nArCOOR
number of esters (aromatic)
functional group
1






counts



1
ESpm15u
Spectral moment 15 from edge adj. matrix
edge adjacency indices
2



1
E2u
2nd component accessibility directional WHIM index/unweighted
WHIM descriptors
3



1
EEig08d
Eigenvalue 08 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
H-051
H attached to alpha-C
atom-centred
1






fragments



1
ESpm14d
Spectral moment 14 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2



1
GATS7m
Geary autocorrelation - lag 7/weighted by atomic masses
2D autocorrelations
2



1
PJI3
3D Petitjean shape index
geometrical descriptors
3



2
X3A
average connectivity index chi-3
connectivity indices
2


Or92a
2
nRCOOR
number of esters (aliphatic)
functional group
1


(ab1A)



counts


(22)
1
Mor10u
3D-MoRSE - signal 10/unweighted
3D-MoRSE descriptors
3



1
Mor04m
3D-MoRSE - signal 04/weighted by atomic masses
3D-MoRSE descriptors
3



1
R1e+
R autocorrelation of lag 1/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities



1
Mor27m
3D-MoRSE - signal 27/weighted by atomic masses
3D-MoRSE descriptors
3



1
nHAcc
number of acceptor atoms for H-bonds (N, O, F)
functional group
1






counts



1
Elm
1st component accessibility directional WHIM index/weighted by atomic
WHIM descriptors
3





masses



1
GATS5m
Geary autocorrelation - lag 5/weighted by atomic masses
2D autocorrelations
2



1
nROH
number of hydroxyl groups
functional group
1






counts



1
R5v
R autocorrelation of lag 5/weighted by atomic van der Waals volumes
GETAWAY descriptors
3



1
Mor10p
3D-MoRSE - signal 10/weighted by atomic polarizabilities
3D-MoRSE descriptors
3



1
C-006
CH2RX
atom-centred
1






fragments



2
Mor11e
3D-MoRSE - signal 11/weighted by atomic Sanderson electronegativities
3D-MoRSE descriptors
3


Or98a
1
Lop
Lopping centric index
topological descriptors
2


(20)
4
O-057
phenol/enol/carboxyl OH
atom-centred
1






fragments



2
B04[C—O]
presence/absence of C—O at topological distance 04
2D binary fingerprints
2



1
GVWAI-80
Ghose-Viswanadhan-Wendoloski drug-like index at 80%
molecular properties
1



1
HATS7p
leverage-weighted autocorrelation of lag 7/weighted by atomic
GETAWAY descriptors
3





polarizabilities



1
HATS5v
leverage-weighted autocorrelation of lag 5/weighted by atomic van der
GETAWAY descriptors
3





Waals volumes



1
MLOGP2
Squared Moriguchi octanol-water partition coeff. (logP{circumflex over ( )}2)
molecular properties
1



2
GATS5e
Geary autocorrelation - lag 5/weighted by atomic Sanderson
2D autocorrelations
2





electronegativities



1
H-049
H attached to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp)
atom-centred
1






fragments



1
MATS8m
Moran autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2



1
nCrs
number of ring secondary C(sp3)
functional group
1






counts



3
HATS3p
leverage-weighted autocorrelation of lag 3/weighted by atomic
GETAWAY descriptors
3





polarizabilities



1
G1s
1st component symmetry directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states



1
S_aasC
S_aasC
atomtypes (Cerius2)
1



1
SP18
shape profile no. 18
Randic molecular
3






profiles



1
B05[C—C]
presence/absence of C—C at topological distance 05
2D binary fingerprints
2



1
JGI2
mean topological charge index of order2
topological charge
2






indices



1
JGI8
mean topological charge index of order8
topological charge
2






indices



1
X4A
average connectivity index chi-4
connectivity indices
2



1
H5e
H autocorrelation of lag 5/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities
















TABLE 4







Top 25 predicted compounds for each Drosophila Or.


Tables contain SMILES strings, and distances, of the top


25 predicted compounds for each Or. All distances represent


the minimum distance based on optimized descriptors to


an active compound for that particular Or.










SMILES
Dist











Or2a










CCSC(C)OC(C)O
0.06547077



CC(C)CCOC(═O)N
0.07017575



CC(C)CC═CC(═O)C
0.08148948



C(CCC)OC(═O)C═C
0.08191658



CCCCSCC(C)O
0.08378222



CCCOC(═O)C(═C)C
0.083826



CC(C)CCOC(═O)C═C
0.0868181



CC(C)CCOC(═O)CS
0.09010645



CCCCOC(═O)CS
0.0962103



CCC(═O)OCCC(C)C
0.09663025



CCCCOC(═O)NC
0.09927085



CC(C)CCOC═O
0.1000939



CCCCOC(═O)N
0.1060414



CCCCOC(═O)CC
0.1064284



CCCNOC(═O)CC
0.1068854



CCCC(═O)CCC#CC
0.1072292



CC(═CC(═C)OC(═O)C)C
0.1073104



CC(═C)C(═O)OCCCN
0.1084215



CCCOC(═O)C(═CC)C
0.1113651



CCC(C)C(═O)OCC═C
0.1126422



CCCCC(═O)C═C(C)C
0.1134776



CCOC(C)OCC#C
0.1143103



CCCCOC(═O)CN
0.1163705



CCC#CC(CC(C)C)O
0.1186069



CCCOC(═O)C═C(C)C
0.1191027







Or7a










CCC═CC═O
0.06287397



CC(═CC)CO
0.08745256



CC(═CCCO)C
0.092048



C1═C(ON═C1)C═O
0.112377



CCC(C)(C)C═NO
0.1149527



CC═CC═CC═O
0.1158738



C1CC(═CC═C1)C═O
0.1165349



C1═COC═C1C═O
0.1266509



C1═C(OC═N1)C═O
0.1277235



CCC(C)CC═O
0.1310643



CCCN═CC═O
0.1384452



CCC═CCO
0.1388489



CCC(═C═CCO)C
0.139234



CCC(═C═CC═O)C
0.1407318



C═C(CO)CC
0.1424594



CC(═C)CCCO
0.1441704



CC(═CCCC═O)C
0.1441953



C═CC(═C)CCC═O
0.1466388



CCCC(═C)CCO
0.1482145



C1CC1CCO
0.148306



C1CC1CO
0.1521086



C1═CC═NC(═C1)C═O
0.153194



CC═CCCCO
0.1548523



CC═CCO
0.1553325



CCCC#CCO
0.1555892







Or9a










CC(CCC═C)O
0.09391671



CC(CCCO)C
0.1227934



CC(C)NC(═O)C
0.1291849



CCC(CCC)O
0.1337314



CC(C)C(C)O
0.1449655



CC═CCOC(═O)C
0.1529503



CCC(═O)NCC#C
0.165569



COCCC═C═N
0.1686873



CC(C)NC(═O)C═C
0.1706617



CC(CCCC)O
0.1771611



C═CC(CCC)O
0.1824546



CCC#CCCO
0.18493



CCC(CC═C)O
0.1958346



CCC(═O)NCC(═O)C
0.2001053



CC(CCOC(═O)C)O
0.2011426



CCC(═O)NCC#N
0.2057452



CC(CC(C)O)C
0.2065425



COC(═O)CC(O)C
0.209219



CC(CC(═O)C)O
0.2110131



CCC(═O)OC(C)(C)O
0.2110216



C═CC(C═C)O
0.2127051



CC(C)C(═O)NC═C
0.2141241



CC(═O)CNCC(═O)C
0.2158649



CCC(═O)NCC
0.2169106



CCC(CC#C)O
0.2179049







Or10a










C1═CC═C(C═C1)C═S═O
0



CC1═C(C(═CC═C1)C)N═S═O
0



CC1═CC(═C(C═C1)OC)C═O
0



CN(C═O)C1═CC═CC═N1
0



CN1C═CC═CC1C═O
0



C1═CC═C(C═C1)C(C═O)C#N
0



C1═CC═C(C(═C1)NC═O)O
0



C1CCC(C1)C(═O)NN
0



C1═CC═C(C═C1)C═C═O
0



CNC(═O)C1═CC═NC═C1
0



CC(═O)NCN(C)C
0



CC(C1═CC═CC═C1)N═C═O
0



CC(═O)C#CC1═CC═CC═C1
0



CC(C)CC(C)C═O
0



C1CCCN(CCC1)N═O
0



C1CCC(CC1)C═O
0



C═CC(═O)C1═CC═CC═C1
0



C1CCN(CC1)N═O
0



C1CCC(CC1)NN═O
0



C═CC(═O)C(═O)C1═CC═CC═C1
0



C1═CC═C(C═C1)CN(C═O)O
0



C1═CC═C(C═C1)C(═O)CS
0



CCN(C═O)C1═CC═CC═N1
0



C1═CC═C(C═C1)C(═S═O)C#N
0



COC(═O)C1═CC═CC═C1N═O
0







OR19a










CC(CCC(CC)O)CC
0.1773688



C═CCCCCC
0.1795821



CCCCCC(═N)C
0.1837474



CCCCC(═O)CC
0.1983858



CCCCCC1CO1
0.2055113



CCCCOC1CC1
0.2097268



CCCCCC1(CO1)C
0.2192309



CCCCCC(═C)C
0.2327693



CCCC(CCC═C)O
0.233932



CCCCCC(C)(C#C)O
0.2437784



CCC(CCCC═C)O
0.2460947



CCCCCC(═O)C═C
0.2539876



CCCCCC(C)S
0.2540855



CCCCC(═O)C(═C)CC
0.2541479



CC(CC)CCCC
0.2605213



CCCC(═O)CCC═C
0.268571



CCCCC(═O)OC(═O)C
0.2821946



CCCC(═O)CCC
0.2839333



CCCCCC(C)(C)O
0.2848814



OC(C(═O)C)CCCCC
0.2870452



CCCCC(CC(═C)C)O
0.2945198



CCCCCC(C)O
0.3036086



CCCCCOC═C
0.304017



CCCCCC(C═C)S
0.3054288



CCCCCC(C)(C═C)O
0.3074979







Or22a










CCCCOC(═O)CCC
0.2657116



CCC#CCCOC(═O)C
0.2798496



CCCCC(═O)OC
0.2807561



CCC(═O)OCC═CC
0.3192234



CCCCC(═O)OCC
0.3386281



C(CC)OC(═O)CCCC
0.3432405



CCCC═CC(═O)OCC
0.3470114



COC(═O)C═CC
0.3564047



CCCOC(═O)CC
0.3620649



CCCC(═O)OCCC
0.3642294



CCCCOC(═O)CC
0.408598



CC(═C)CCCC(═O)OC
0.4087118



CCCCCC(═O)OCCC
0.4096699



CCC═CCCOC(═O)C
0.4280228



CCC═CCC(═O)OCC
0.4509044



COC(═O)CCC═CCCC
0.4515538



COC(═O)C═CCC
0.4606538



CCC#CC(═O)OC
0.4635536



CCCCCOC(═O)CC
0.465345



CC(═CCOC(═O)C)C
0.4677529



COC(═O)CC═CC
0.4684388



CCC═CC(═O)OCC
0.4687615



CC(═C)CCCOC(═O)C
0.4696284



CC(═COC(═O)C)C
0.4710929



C(C)OC(═O)CCCCCC
0.4714722







Or23a










CCCCC═CCO
0.4489215



C(CCC═CCC)O
0.4645003



C═CCCCCCO
0.4966429



CC═CC═CCO
0.5369311



CC(CCC═C)O
0.5705127



C#CCCCCO
0.653074



CCCCCOO
0.6743714



C(CCCCCC)O
0.679884



C#CCCCCCO
0.6854802



COC═CCCCO
0.6878323



C(C═CC═CCC)O
0.730026



CC═CC═CCCO
0.7327627



CC(CCCC═C)O
0.7331066



CCCC(C)CCO
0.7638355



CCCC#CC#CO
0.7642293



CCCCOO
0.8340461



CCC#CCCCO
0.8383432



CCCC═CCCO
0.8559539



CC(CCC#C)O
0.8633463



CCCCCCOO
0.8935004



CCCCCC#CO
0.895056



CC(CCC═C═C)O
0.913551



CCC═CCO
0.9216458



CC#CCCCCO
0.9630825



CCC#CCCO
0.9669537







Or35a










CCC#CCCO
0.150652



CCC═CCCOC(═O)C
0.153849



C═CCCCCO
0.1577711



C(CCCCCC═C)O
0.1896431



CCC═CC═O
0.1996007



CCC#CCCOC(═O)C
0.203439



CCCC#CCO
0.2051737



CC(═O)OCCCCC═C
0.2169564



CC═CCCCO
0.2327925



C(CCC═CC)OC(═O)C
0.2366404



C(CCC═C)O
0.253342



CC(═O)OCCCC═C
0.2575001



C#CCCCCO
0.258166



C(CCCCCCCC)O
0.262205



C(CCO)CCS
0.2659421



CC═CC═CCOC(═O)C
0.277348



C(CC═C═O)CS
0.2857512



CC(═O)OCCCC═C═C
0.2908757



CCC═CCO
0.2957919



CC(═O)OCCCCC#C
0.3021282



C#CCCCCCCO
0.3034524



CCCC#CC═O
0.3056329



C═CCCCCCCCO
0.3066109



C(CC═CC)O
0.3164314



CCC═CCCCCO
0.3186713







Or43a










CCCCCC(C#C)O
0.00332052



C1CC1CCCCO
0.00699056



CCC#CCC(CC)O
0.01572877



CCCCC(CC)O
0.01572877



CCCCCC(CC)O
0.01642782



CCCCC(C)CO
0.01782593



CCC#CC(CC)O
0.0180007



C═CCC(C═C)O
0.01835023



CCC#CCCO
0.01887452



CC(═C)CCCO
0.01904928



C═CC(CCCC)O
0.02114645



CCCC#CCO
0.0223698



CCC#CC(C)O
0.0223698



CCCCCC(C)O
0.0223698



CC═CC═CCO
0.02341839



CCC═CCO
0.02376791



CC1(CC1)CCCO
0.02499126



CC#CCCO
0.02603985



CC═C═CC(C)O
0.02638937



C═CCCCC(C═C)O
0.0267389



CC(C)CCCCO
0.02743796



CCC(CCC(C)C)O
0.02743796



CCC(C#CC)O
0.02761272



CCCCC(C#C)O
0.02778749



CCCCC(C═CC)O
0.02778749







Or43b










CCCONC(═O)C
0.0959588



CCN(C(═O)C)O
0.1130635



CCOC(═O)SCC
0.1132685



CCNNC(═O)C
0.1183047



CC#CC(NC)O
0.1231371



CC═C═CC(C)(C)O
0.1294547



CCNCNC(═O)C
0.1317853



CCCC(O)OCC
0.1391272



CCC(═O)NCC
0.1435372



CCCC(═O)NCC
0.1476275



CCC(O)OCC
0.1478191



CCNC(═O)OCC
0.1489972



CC#CC(═O)NC
0.1490445



CC(CN)NC(═O)C
0.1502223



CCNC(═O)C
0.1502272



CCCC(═O)NC
0.1678103



CCOC(C)ON
0.1752449



CC(C)OC(C)C#N
0.1764415



CC(═O)C═CCC
0.1765129



CC(CNC(═O)C)O
0.1766655



CC(C)C═CC(═O)C
0.1781594



CC═CC(═O)C
0.181488



CCOC(C)O
0.1828469



CCOC(C)OC#N
0.1867745



CCNC(═O)NCC
0.1871594







Or47a










CCCCCCC(═O)C
0.00616096



CCC(CC)CCC(═O)C
0.00770119



CCCSCO
0.00847131



CCCOC#N
0.00924143



CCOCC#N
0.00924143



CCCCC(═O)COC
0.01001155



CCCCCCC(═O)C═O
0.01078167



CCCCC(═O)CC(═O)C
0.01078167



CCCCC(═O)OC
0.01078167



CC(C)C(C)COC(═O)C
0.01155179



CC(C)CCC(═O)OC
0.01155179



CCCCCOC(═O)C═C
0.01232191



CCCCCOC(═O)CC
0.01232191



CC(C)COC#N
0.01309203



CCCCCCC(═O)OC
0.01309203



CCCCCCSSC
0.01386215



CCCCC(C)CC(═O)C
0.01386215



CCCCCC(═O)C═C
0.01386215



CCCCCCC(═O)CO
0.01386215



CC(C)OCC#N
0.01386215



CCCCCC(═O)C═C
0.01386215



CC(═O)OCC(C)(C)C
0.01463227



CCCCSS(═O)C
0.01463227



CCCCCSSC
0.01540239



CCCCCC(═O)OC═C
0.01540239







Or49b










C1═CC═C(C═C1)N═O
0.01680465



CC1═CC(═CC═C1)O
0.02191376



C1═CC═C(C(═C1)O)S
0.05712351



Cc1ccc(cc1)O
0.08002216



C1═CC(═CC═C1N)O
0.08291527



C1═CC═C(C═C1)N═C═O
0.08630082



C1═CC═C(C(═C1)N)O
0.08697793



C1═CC(═CC(═C1)O)N
0.08747038



C1═CC═C(C(═C1)O)C1
0.08876304



c1(ccccc1)NC═O
0.09744237



C1═CC(═CC═C1O)S
0.09756548



C1═CC═C(C═C1)C#CO
0.1029824



C1═CC═C(C═C1)C═C═O
0.1066141



CC1═C(C═CC═C1O)N
0.1069219



CC1═CC(═C(C═C1)C)O
0.1087686



CC1═C(C(═CC═C1)O)C
0.1101844



C═C(C1═CC═CC═C1)O
0.1105537



CC1═C(C═CC═C1S)O
0.1107999



C1═CC═C(C═C1)NN═O
0.1138777



CC1═C(C═C(C═C1)N)O
0.1143086



C1═CC(═CC(═C1)O)O
0.1145548



C1═CC═C(C═C1)C═CO
0.1148626



CC1═C(C(═CC═C1)O)S
0.1148626



C1═CC═C(C═C1)NO
0.1188637



C1═CC(═CC═C1O)O
0.1207719







Or59b










CCC(═O)OC(C)O
0.08309379



CCC(═O)OC
0.08527063



C(C)OC(═O)CC
0.09857435



CCC(═O)COC
0.1112024



CCCOC(═O)C
0.1141674



CCCOC(═O)C
0.1141674



CCC(O)OC(═O)C
0.1244704



CC(COC(═O)C)O
0.1292618



CCC(C(═O)OC)O
0.1352768



CCC(N═C═O)OC
0.1459781



COC(═O)CC(O)C
0.1504875



CCCS(═O)OC
0.1531444



CCC(═O)C(O)OC
0.1567203



CCCC(═O)N(C)O
0.1589646



CC(CC(═O)C)O
0.1612506



CCC(═O)CC
0.1613363



CC(N═C═O)OC
0.1654712



OC(C)C(═O)CC
0.165828



CC(O)S(═O)C
0.1659486



COCC(═O)C
0.1665356



CCN(C)N═O
0.1718486



CC(═O)CCOC
0.1721226



CCC#CC(═O)C
0.1737079



CCS(═O)OC
0.1760367



CCC(═O)OOC
0.1770918







Or67a










C═CC(═O)C1═CC═CC═N1
0.3008233



C1═CC═C(C(═C1)CC#N)C═O
0.3080015



CCOC(═N)C1═CC═CC═C1
0.312236



COC(═N)C1═CC═CC═C1
0.3311703



CCC1═COC(═N1)CC
0.3703241



CCC(═O)C1═CC═CC═N1
0.3768891



C1═CC═C(C(═C1)C═O)C═O
0.3797241



CCOC(═O)N1C═CC═C1
0.3857737



C1═CC(═CC(═C1)C═O)C═O
0.3905579



CC1═CC(═CC═C1)CO
0.3917814



CCOCC1═CC═CC═C1
0.399528



COC(═O)N1C═CC═C1
0.4010939



COC(c1ccccc1)O
0.4035766



C1═CC═C(C═C1)C2═CC═NO2
0.4060794



c1(ccccc1)COC
0.4097667



COC1═NN═CC2═CC═CC═C21
0.4106803



COC(═O)C1═CC═CC═C1C#N
0.4139384



CC1═NOCC2═CC═CC═C12
0.4173282



COC1═NC═C(C═C1)C═C
0.419944



CC(═CCOC)C
0.4208605



CCC═CCC(═O)C
0.4243553



CC1═CC2═CC═CC═C2CO1
0.4298624



COC1═NN═C2N1C3═CC═CC═C3O2
0.4389819



CC1═CC═C(C═C1)C(═O)OC
0.4404134



C1═CC(═O)C═CC2═C1C═CO2
0.4413276







Or67c










CC(CC#C)O
0.07067509



C═CCC(C═C═C)O
0.0775118



CC(C)(CC═C)O
0.0885166



C═CCCCCO
0.09353587



CC(═C)CCCO
0.1018462



CC(CCCC)O
0.1056086



CCC(CC═C)O
0.1068447



CCC#CCCCO
0.1081803



CCC(CC#C)O
0.1259778



CC═C(C)C(C)O
0.1262036



CC═CCCCO
0.1270505



CC(CC═C)CO
0.1274556



CCC(CCC)O
0.1279088



CC(C)(CC#C)O
0.1294606



CC(CCO)C═C
0.1341464



CC═C═CCCCO
0.1372657



CC(C)C(C#C)O
0.1429075



CCC#CCC(C)O
0.1430763



CCC(═C)C(C)O
0.1438052



CCC(C)(CC═C)O
0.1477914



CCCCC(═C)CO
0.1527974



CCC(═CC)CO
0.1538365



CC(CC═C)O
0.1609561



CCC(C(C)C)O
0.1618278



CCCC(C)CO
0.1653694







Or85a










CCC#CCCO
0.09486577



CC═CCCCO
0.1241049



CCCC(═N)OCC
0.1455693



CCCCNC(═C)C
0.1695939



CCCC#CCO
0.1791638



CCCC(═O)CCO
0.1893542



CC═C═CCCCO
0.1938411



CCN═C(C)CC(═C)O
0.1971383



CC═CC═CCCO
0.1971396



CCOC#CC(C)O
0.2069597



CCC#CCCCO
0.2311878



CCCC(O)OCC
0.2512179



CCNC(C)OC═C
0.255045



CCC(═O)NCC(═O)C
0.2887675



CC(CCCO)C═C
0.289174



CC(C)(CCCOC)O
0.2891912



CCOC(═O)C(C)OC
0.294917



CCOC(C)OC(═C)O
0.297801



CCCNC(C)C═O
0.3026955



CC(C(C)OCC═C)O
0.3104233



CC═CC═CCO
0.312425



OCCCCC(═O)C
0.3180066



CCCCC(═C)CO
0.3214983



CCC(═O)NCC(═C)C
0.331915



CCOC(C)C(O)OC
0.3407239







Or85b










CC(CCCC═C)CO
0.04010449



CCCCCCC═O
0.0541304



CCC(═O)C═CCCC
0.05661388



CCCCC═CC(═O)C
0.05802127



C(CCCCCC═C)O
0.06257155



CCCCCC(═O)CC
0.06590403



CC(C(═O)C)CCCC
0.0741716



CN(CCCC═C)O
0.08071376



CC(CCC═C═C)C═O
0.08460999



CCCCC(═C)C═O
0.08656348



CCC(CCC#CC)O
0.0872067



CN(CCCCC═C)O
0.08897917



CCCCC1CCOC1═O
0.09145651



C═CCCCCO
0.09536294



CCCCC═CCO
0.09564121



CCC(CCC═C═C)O
0.0958081



CC(C)CCCCOC
0.09698922



CCCCCC(CC)O
0.0993378



CC(═CC)CCC(═O)C
0.1024115



CCCCC(C)C(C)O
0.1036823



CCCCC(C)C(C)O
0.1036823



CCCCC#CC(C)O
0.1038561



CC(C)CC═CC(═O)C
0.1081452



CCC(C)CCC(C)O
0.1082218



CC(CCC═C)N(C)O
0.1085992







Or85f










CC(CCC═C)O
0.3215251



CCCC#CCO
0.3977383



CCC#CCCO
0.4721775



CCCC(═O)OC(C)O
0.5291351



COC(═O)CC(O)C
0.5396708



CCCC(COC)O
0.5401751



CC(CCCC═C)O
0.574608



CC(CC(═O)OC═C)O
0.5760439



CC═CCCCO
0.5830891



CCCC(═O)N(C)O
0.5926106



CN(CCCCO)N═O
0.6121783



CC(C)(C1C(O1)C#C)O
0.6193232



C(CC═CC)O
0.6211374



CC(CCC═C═C)O
0.6297574



CCC(C(═O)OCC)O
0.6342998



CC(═O)OC(COC)O
0.6391873



CCCC1C(O1)CO
0.6497865



CCOC(═O)CC(C)O
0.6566422



CC(C#COC═C)O
0.663147



CN(CCCC═C)O
0.6794308



C═CC(CCCC)O
0.6991725



CCC(COC═C)O
0.7054714



CCCCCN(C)O
0.7137121



CC(COCCC#N)O
0.7174705



CC(CCO)C═C
0.7219598







Or98a










CC(CCCCO)C═C
0



CCCC(═O)OCNO
0



CC(CC═C═C(C)C)O
0



CNCC(═O)OCCO
0



CC(C)COCC(C)O
0.0006135



CC(═O)OC═CC═C
0.0006135



C═CCCCC(═C)CO
0.0006135



C═CCOCC(C═C)O
0.0006135



C(CN)C(═O)OCCO
0.0006135



C═CC(COCC#C)O
0.0006135



CCOCCC(C)O
0.0006135



CC(CC(═O)OCCO)O
0.0006135



CCOC(═O)CC(═O)C
0.0006135



CCOC#CC(C)O
0.00122699



CC(C)(C#CCN(C)C)O
0.00122699



CCC(═O)COCC
0.00122699



CC(═O)CC(═O)OCCO
0.00122699



CC(C)OC(═O)NCO
0.00122699



CCOC(═O)CS(═O)C
0.00122699



CC(CO)OCCC═C
0.00122699



CCCNOC(═O)CC
0.00122699



CCCC(═O)OC(═O)C
0.00122699



CC(═C)COCCOC
0.00122699



CC(═O)OC(═O)CC═C
0.00122699



CCOCOC(═O)C
0.00184049







ab1A










CC(═O)C(═O)C
0.02090025



CC(OCC(C)C)═O
0.05702



CC(═O)OCCC#C
0.07507874



CCC(═O)OOCC
0.07770847



CCOC(═O)OC
0.07784295



CCCOC(═O)CC
0.08788487



CCCOC(═O)OC
0.09563759



CCOC(═O)CC#C
0.09593777



CCC(═O)OCC#C
0.1027616



CC1CC(═O)OC1
0.1040388



COCCC(═O)OC
0.1130966



CCCC(OC)═O
0.11606



CCSCC(C)C═O
0.116945



CC1COC(═O)O1
0.1170738



CCCOC(═O)C
0.1177654



C(C)OC(═O)OCC
0.1181321



CC(C)OC═O
0.1209444



CC(C)CCS(═O)C
0.1236193



CCC(C)OCC═O
0.1263356



CCOCS(═O)C
0.1272632



CCOC(═O)ONN
0.1288552



CCC(═O)OC
0.1298152



CC1OCC(═O)O1
0.1309472



CC(═O)OCC#C
0.1313023



COC(═O)CCCS
0.132371

















TABLE 5





Optimized descriptor sets for each Mammalian OR. Optimized descriptors occurrences, symbol, brief description, class,


and dimensionality are listed. Descriptors are listed in ascending order of when they were selected into the optimized


set. Weights indicate the number of times a descriptor was selected in an optimized descriptor set. A summary of


the total number of descriptors selected for the receptor repertoire is provided as the beginning.







Mammalian Descriptor Lists





Descriptor Class Type Counts for all Org










GETAWAY descriptors
109



atom-centred fragments
49



2D autocorrelations
48



RDF descriptors
48



3D-MoRSE descriptors
46



WHIM descriptors
43



functional group counts
33



2D binary fingerprints
26



Burden eigenvalues
23



edge adjacency indices
21



geometrical descriptors
21



topological descriptors
14



2D frequency fingerprints
13



topological charge indices
12



atomtypes (Cerius2)
11



molecular properties
11



walk and path counts
7



constitutional descriptors
6



Randic molecular profiles
5



topological (Cerius2)
4



information indices
4



connectivity indices
3



structural (Cerius2)
1



eigenvalue-based indices
1



charge descriptors
0







Dimensionality Counts (Weights Included)










Num zero dimensional descriptors:
7



Num one dimensional descriptors:
104



Num two dimensional descriptors:
176



Num three dimensional descriptors:
272







Origin (Weights Included)










Num Dragon descriptors:
546



Num Cerius2 descriptors:
13







Dimensionality Counts (Weights Excluded)










Num zero dimensional descriptors:
7



Num one dimensional descriptors:
37



Num two dimensional descriptors:
93



Num three dimensional descriptors:
155







Origin (Weights Excluded)










Num unique Dragon descriptors:
284



Num unique Cerius2 descriptors:
8











MOR1.1












844
2
Mor17m
3D-MoRSE - signal 17/weighted by atomic masses
3D-MoRSE descriptors
3


1300
8
H-051
H attached to alpha-C
atom-centred fragments
1


1248
2
R6p+
R maximal autocorrelation of lag 6/weighted by atomic
GETAWAY descriptors
3





polarizabilities


914
4
Mor23e
3D-MoRSE - signal 23/weighted by atomic Sanderson
3D-MoRSE descriptors
3





electronegativities


857
5
Mor30m
3D-MoRSE - signal 30/weighted by atomic masses
3D-MoRSE descriptors
3


1211
4
R5v+
R maximal autocorrelation of lag 5/weighted by atomic van der
GETAWAY descriptors
3





Waals volumes


923
1
Mor32e
3D-MoRSE - signal 32/weighted by atomic Sanderson
3D-MoRSE descriptors
3





electronegativities


519
1
JGI7
mean topological charge index of order7
topological charge indices
2


1019
2
E1s
1st component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic electrotopological states


1254
1
nCt
number of total tertiary C(sp3)
functional group counts
1


1270
1
nArCO
number of ketones (aromatic)
functional group counts
1


1304
1
O-058
#NAME?
atom-centred fragments
1


1344
1
B07[C—O]
presence/absence of C—O at topological distance 07
2D binary fingerprints
2


302
2
GATS2m
Geary autocorrelation - lag 2/weighted by atomic masses
2D autocorrelations
2


756
1
RDF110e
Radial Distribution Function - 11.0/weighted by atomic Sanderson
RDF descriptors
3





electronegativities


1262
1
nCconj
number of non-aromatic conjugated C(sp2)
functional group counts
1


1282
1
C-006
CH2RX
atom-centred fragments
1


1256
1
nCrs
number of ring secondary C(sp3)
functional group counts
1


307
1
GATS7m
Geary autocorrelation - lag 7/weighted by atomic masses
2D autocorrelations
2


1280
1
C-003
CHR3
atom-centred fragments
1


276
1
MATS8m
Moran autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2







MOR106.1












948
1
Mor25p
3D-MoRSE - signal 25/weighted by atomic polarizabilities
3D-MoRSE descriptors
3


476
1
BEHe6
highest eigenvalue n. 6 of Burden matrix/weighted by atomic
Burden eigenvalues
2





Sanderson electronegativities


212
1
IC1
information content index (neighborhood symmetry of 1-order)
information indices
2


1282
2
C-006
CH2RX
atom-centred fragments
1


1233
2
RTe+
R maximal index/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities


147
1
piPC07
molecular multiple path count of order 07
walk and path counts
2


743
1
RDF045e
Radial Distribution Function - 4.5/weighted by atomic Sanderson
RDF descriptors
3





electronegativities


1266
1
nRCOOH
number of carboxylic acids (aliphatic)
functional group counts
1


1213
1
R7v+
R maximal autocorrelation of lag 7/weighted by atomic van der
GETAWAY descriptors
3





Waals volumes


630
1
HOMT
HOMA total
geometrical descriptors
3


683
1
RDF035m
Radial Distribution Function - 3.5/weighted by atomic masses
RDF descriptors
3


1298
1
H-049
H attached to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp)
atom-centred fragments
1


608
1
SHP2
average shape profile index of order 2
Randic molecular profiles
3







MOR107.1












1255
1
nCq
number of total quaternary C(sp3)
functional group counts
1



5


866
1
Mor07v
3D-MoRSE - signal 07/weighted by atomic van der Waals
3D-MoRSE descriptors
3





volumes


465
1
BELv3
lowest eigenvalue n. 3 of Burden matrix/weighted by atomic van
Burden eigenvalues
2





der Waals volumes


1246
1
R4p+
R maximal autocorrelation of lag 4/weighted by atomic
GETAWAY descriptors
3





polarizabilities


964
1
E1u
1st component accessibility directional WHIM index/unweighted
WHIM descriptors
3


516
1
JGI4
mean topological charge index of order4
topological charge indices
2


635
1
DISPv
d COMMA2 value/weighted by atomic van der Waals volumes
geometrical descriptors
3


29
1
nR06
number of 6-membered rings
constitutional descriptors
0


684
1
RDF040m
Radial Distribution Function - 4.0/weighted by atomic masses
RDF descriptors
3


1232
1
R8e+
R maximal autocorrelation of lag 8/weighted by atomic
GETAWAY descriptors
3





Sanderson electronegativities


147
1
piPC07
molecular multiple path count of order 07
walk and path counts
2


1012
1
L2s
2nd component size directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states


148
1
piPC08
molecular multiple path count of order 08
walk and path counts
2


22
1
nDB
number of double bonds
constitutional descriptors
0


1300
1
H-051
H attached to alpha-C
atom-centred fragments
1


975
1
E1m
1st component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic masses


1338
1
B05[C—O]
presence/absence of C—O at topological distance 05
2D binary fingerprints
2


497
1
BELp3
lowest eigenvalue n. 3 of Burden matrix/weighted by atomic
Burden eigenvalues
2





polarizabilities







MOR129.1












1045
1
Dv
D total accessibility index/weighted by atomic van der Waals
WHIM descriptors
3





volumes


1136
2
HATS7e
leverage-weighted autocorrelation of lag 7/weighted by atomic
GETAWAY descriptors
3





Sanderson electronegativities


1355
1
F01[C—C]
frequency of C—C at topological distance 01
2D frequency fingerprints
2


805
1
Mor10u
3D-MoRSE - signal 10/unweighted
3D-MoRSE descriptors
3


1094
1
HATS5m
leverage-weighted autocorrelation of lag 5/weighted by atomic
GETAWAY descriptors
3





masses


1100
2
H1v
H autocorrelation of lag 1/weighted by atomic van der Waals
GETAWAY descriptors
3





volumes


870
1
Mor11v
3D-MoRSE - signal 11/weighted by atomic van der Waals
3D-MoRSE descriptors
3





volumes


1337
1
B05[C—C]
presence/absence of C—C at topological distance 05
2D binary fingerprints
2


751
1
RDF085e
Radial Distribution Function - 8.5/weighted by atomic Sanderson
RDF descriptors
3





electronegativities


1044
1
Dm
D total accessibility index/weighted by atomic masses
WHIM descriptors
3


1079
1
H0m
H autocorrelation of lag 0/weighted by atomic masses
GETAWAY descriptors
3


901
1
Mor10e
3D-MoRSE - signal 10/weighted by atomic Sanderson
3D-MoRSE descriptors
3





electronegativities


107
1
D/Dr06
distance/detour ring index of order 6
topological descriptors
2


1095
1
HATS6m
leverage-weighted autocorrelation of lag 6/weighted by atomic
GETAWAY descriptors
3





masses


297
1
MATS5p
Moran autocorrelation - lag 5/weighted by atomic polarizabilities
2D autocorrelations
2


683
1
RDF035m
Radial Distribution Function - 3.5/weighted by atomic masses
RDF descriptors
3


1126
1
H7e
H autocorrelation of lag 7/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities


1099
1
H0v
H autocorrelation of lag 0/weighted by atomic van der Waals
GETAWAY descriptors
3





volumes


1184
1
R5m
R autocorrelation of lag 5/weighted by atomic masses
GETAWAY descriptors
3







MOR136.1












682
1
RDF030m
Radial Distribution Function - 3.0/weighted by atomic masses
RDF descriptors
3


1466
1
S_dssC
S_dssC
atomtypes (cerius2)
1


832
1
Mor05m
3D-MoRSE - signal 05/weighted by atomic masses
3D-MoRSE descriptors
3


479
1
BELe1
lowest eigenvalue n. 1 of Burden matrix/weighted by atomic
Burden eigenvalues
2





Sanderson electronegativities


1175
1
R5u+
R maximal autocorrelation of lag 5/unweighted
GETAWAY descriptors
3


608
1
SHP2
average shape profile index of order 2
Randic molecular profiles
3







MOR139.1












1100
2
H1v
H autocorrelation of lag 1/weighted by atomic van der Waals
GETAWAY descriptors
3





volumes


1070
1
HATS1u
leverage-weighted autocorrelation of lag 1/unweighted
GETAWAY descriptors
3


1310
1
TPSA(NO)
topological polar surface area using N, O polar contributions
molecular properties
1


146
1
piPC06
molecular multiple path count of order 06
walk and path counts
2


1087
1
H8m
H autocorrelation of lag 8/weighted by atomic masses
GETAWAY descriptors
3


1316
1
GVWAI-80
Ghose-Viswanadhan-Wendoloski drug-like index at 80%
molecular properties
1


1198
1
R1v
R autocorrelation of lag 1/weighted by atomic van der Waals
GETAWAY descriptors
3





volumes


302
1
GATS2m
Geary autocorrelation - lag 2/weighted by atomic masses
2D autocorrelations
2


915
1
Mor24e
3D-MoRSE - signal 24/weighted by atomic Sanderson
3D-MoRSE descriptors
3





electronegativities


358
1
EEig09d
Eigenvalue 09 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2







MOR162.1












627
1
HOMA
Harmonic Oscillator Model of Aromaticity index
geometrical descriptors
3


1094
1
HATS5m
leverage-weighted autocorrelation of lag 5/weighted by atomic
GETAWAY descriptors
3





masses


998
1
E2e
2nd component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic Sanderson electronegativities


1121
1
H2e
H autocorrelation of lag 2/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities


1212
1
R6v+
R maximal autocorrelation of lag 6/weighted by atomic van der
GETAWAY descriptors
3





Waals volumes


993
1
P2e
2nd component shape directional WHIM index/weighted by
WHIM descriptors
3





atomic Sanderson electronegativities


297
1
MATS5p
Moran autocorrelation - lag 5/weighted by atomic polarizabilities
2D autocorrelations
2


628
1
RCI
Jug RC index
geometrical descriptors
3


1095
1
HATS6m
leverage-weighted autocorrelation of lag 6/weighted by atomic
GETAWAY descriptors
3





masses


683
1
RDF035m
Radial Distribution Function - 3.5/weighted by atomic masses
RDF descriptors
3


1120
1
H1e
H autocorrelation of lag 1/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities







MOR170.1












1290
3
C-025
R—CR—R
atom-centred fragments
1


1371
1
F05[C—O]
frequency of C—O at topological distance 05
2D frequency fingerprints
2


1212
1
R6v+
R maximal autocorrelation of lag 6/weighted by atomic van der
GETAWAY descriptors
3





Waals volumes


998
2
E2e
2nd component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic Sanderson electronegativities


1464
1
S_aaCH
S_aaCH
atomtypes (cerius2)
1


1233
2
RTe+
R maximal index/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities


1178
1
R8u+
R maximal autocorrelation of lag 8/unweighted
GETAWAY descriptors
3


262
1
ATS2p
Broto-Moreau autocorrelation of a topological structure - lag 2/
2D autocorrelations
2





weighted by atomic polarizabilities


297
1
MATS5p
Moran autocorrelation - lag 5/weighted by atomic polarizabilities
2D autocorrelations
2


714
1
RDF045v
Radial Distribution Function - 4.5/weighted by atomic van der
RDF descriptors
3





Waals volumes


1004
1
P2p
2nd component shape directional WHIM index/weighted by
WHIM descriptors
3





atomic polarizabilities


1249
1
R7p+
R maximal autocorrelation of lag 7/weighted by atomic
GETAWAY descriptors
3





polarizabilities


1184
1
R5m
R autocorrelation of lag 5/weighted by atomic masses
GETAWAY descriptors
3


627
1
HOMA
Harmonic Oscillator Model of Aromaticity index
geometrical descriptors
3







MOR184.1












1461
1
S_dCH2
S_dCH2
atomtypes (cerius2)
1


301
1
GATS1m
Geary autocorrelation - lag 1/weighted by atomic masses
2D autocorrelations
2


1297
1
H-047
H attached to C1(sp3)/C0(sp2)
atom-centred fragments
1


37
1
Qindex
Quadratic index
topological descriptors
2


635
1
DISPv
d COMMA2 value/weighted by atomic van der Waals volumes
geometrical descriptors
3


979
1
L2v
2nd component size directional WHIM index/weighted by atomic
WHIM descriptors
3





van der Waals volumes


18
1
nCIC
number of rings
constitutional descriptors
0


1111
1
HATS2v
leverage-weighted autocorrelation of lag 2/weighted by atomic
GETAWAY descriptors
3





van der Waals volumes


802
1
Mor07u
3D-MoRSE - signal 07/unweighted
3D-MoRSE descriptors
3


1222
1
R7e
R autocorrelation of lag 7/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities


136
1
MPC06
molecular path count of order 06
walk and path counts
2


373
1
EEig09r
Eigenvalue 09 from edge adj. matrix weighted by resonance
edge adjacency indices
2





integrals


19
1
nCIR
number of circuits
constitutional descriptors
0


685
1
RDF045m
Radial Distribution Function - 4.5/weighted by atomic masses
RDF descriptors
3


497
1
BELp3
lowest eigenvalue n. 3 of Burden matrix/weighted by atomic
Burden eigenvalues
2





polarizabilities


358
1
EEig09d
Eigenvalue 09 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2


1001
1
L2p
2nd component size directional WHIM index/weighted by atomic
WHIM descriptors
3





polarizabilities


1156
1
HATS7p
leverage-weighted autocorrelation of lag 7/weighted by atomic
GETAWAY descriptors
3





polarizabilities


1246
1
R4p+
R maximal autocorrelation of lag 4/weighted by atomic
GETAWAY descriptors
3





polarizabilities


837
1
Mor10m
3D-MoRSE - signal 10/weighted by atomic masses
3D-MoRSE descriptors
3







MOR185.1












103
1
BAC
Balaban centric index
topological descriptors
2


1091
1
HATS2m
leverage-weighted autocorrelation of lag 2/weighted by atomic
GETAWAY descriptors
3





masses


1178
1
R8u+
R maximal autocorrelation of lag 8/unweighted
GETAWAY descriptors
3


168
1
X5A
average connectivity index chi-5
connectivity indices
2


997
1
E1e
1st component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic Sanderson electronegativities


1233
1
RTe+
R maximal index/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities


998
1
E2e
2nd component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic Sanderson electronegativities


302
1
GATS2m
Geary autocorrelation - lag 2/weighted by atomic masses
2D autocorrelations
2


1140
1
H1p
H autocorrelation of lag 1/weighted by atomic polarizabilities
GETAWAY descriptors
3


1156
1
HATS7p
leverage-weighted autocorrelation of lag 7/weighted by atomic
GETAWAY descriptors
3





polarizabilities


683
1
RDF035m
Radial Distribution Function - 3.5/weighted by atomic masses
RDF descriptors
3


608
1
SHP2
average shape profile index of order 2
Randic molecular profiles
3


1244
1
R2p+
R maximal autocorrelation of lag 2/weighted by atomic
GETAWAY descriptors
3





polarizabilities







MOR189.1












1256
1
nCrs
number of ring secondary C(sp3)
functional group counts
1


1457
1
V-DIST-
V-DIST-mag
topological (cerius2)
2




mag


610
1
J3D
3D-Balaban index
geometrical descriptors
3


1413
2
Atype_C_40
Number of Carbon Type 40
atomtypes (Cerius2)
1


375
1
EEig11r
Eigenvalue 11 from edge adj. matrix weighted by resonance
edge adjacency indices
2





integrals


1183
1
R4m
R autocorrelation of lag 4/weighted by atomic masses
GETAWAY descriptors
3


930
1
Mor07p
3D-MoRSE - signal 07/weighted by atomic polarizabilities
3D-MoRSE descriptors
3


1316
1
GVWAI-80
Ghose-Viswanadhan-Wendoloski drug-like index at 80%
molecular properties
1


681
1
RDF025m
Radial Distribution Function - 2.5/weighted by atomic masses
RDF descriptors
3


1343
1
B07[C—C]
presence/absence of C—C at topological distance 07
2D binary fingerprints
2


1174
1
R4u+
R maximal autocorrelation of lag 4/unweighted
GETAWAY descriptors
3


913
1
Mor22e
3D-MoRSE - signal 22/weighted by atomic Sanderson
3D-MoRSE descriptors
3





electronegativities


1304
1
O-058
#NAME?
atom-centred fragments
1


356
1
EEig07d
Eigenvalue 07 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2


360
1
EEig11d
Eigenvalue 11 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2







MOR2.1












685
1
RDF045m
Radial Distribution Function - 4.5/weighted by atomic masses
RDF descriptors
3


1316
2
GVWAI-80
Ghose-Viswanadhan-Wendoloski drug-like index at 80%
molecular properties
1


485
1
BELe7
lowest eigenvalue n. 7 of Burden matrix/weighted by atomic
Burden eigenvalues
2





Sanderson electronegativities


686
1
RDF050m
Radial Distribution Function - 5.0/weighted by atomic masses
RDF descriptors
3


905
1
Mor14e
3D-MoRSE - signal 14/weighted by atomic Sanderson
3D-MoRSE descriptors
3





electronegativities


346
1
EEig12x
Eigenvalue 12 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2


843
1
Mor16m
3D-MoRSE - signal 16/weighted by atomic masses
3D-MoRSE descriptors
3


376
2
EEig12r
Eigenvalue 12 from edge adj. matrix weighted by resonance
edge adjacency indices
2





integrals


949
1
Mor26p
3D-MoRSE - signal 26/weighted by atomic polarizabilities
3D-MoRSE descriptors
3


804
1
Mor09u
3D-MoRSE - signal 09/unweighted
3D-MoRSE descriptors
3


1262
1
nCconj
number of non-aromatic conjugated C(sp2)
functional group counts
1


845
1
Mor18m
3D-MoRSE - signal 18/weighted by atomic masses
3D-MoRSE descriptors
3


1173
1
R3u+
R maximal autocorrelation of lag 3/unweighted
GETAWAY descriptors
3


1344
1
B07[C—O]
presence/absence of C—O at topological distance 07
2D binary fingerprints
2


1358
1
F02[C—C]
frequency of C—C at topological distance 02
2D frequency fingerprints
2







MOR203.1












1340
6
B06[C—C]
presence/absence of C—C at topological distance 06
2D binary fingerprints
2


1298
1
H-049
H attached to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp)
atom-centred fragments
1


931
1
Mor08p
3D-MoRSE - signal 08/weighted by atomic polarizabilities
3D-MoRSE descriptors
3


1390
1
Hbond
Number of Hydrogen bond acceptors
structural (Cerius2)
0




acceptor


661
1
RDF075u
Radial Distribution Function - 7.5/unweighted
RDF descriptors
3


1203
1
R6v
R autocorrelation of lag 6/weighted by atomic van der Waals
GETAWAY descriptors
3





volumes


1268
3
nRCHO
number of aldehydes (aliphatic)
functional group counts
1


1266
2
nRCOOH
number of carboxylic acids (aliphatic)
functional group counts
1


272
1
MATS4m
Moran autocorrelation - lag 4/weighted by atomic masses
2D autocorrelations
2


1018
1
G3s
3st component symmetry directional WHIM index/weighted by
WHIM descriptors
3





atomic electrotopological states


106
1
D/Dr05
distance/detour ring index of order 5
topological descriptors
2


1270
1
nArCO
number of ketones (aromatic)
functional group counts
1


1352
1
B10[C-C]
presence/absence of C-C at topological distance 10
2D binary fingerprints
2


274
1
MATS6m
Moran autocorrelation - lag 6/weighted by atomic masses
2D autocorrelations
2


445
1
BEHm7
highest eigenvalue n. 7 of Burden matrix/weighted by atomic
Burden eigenvalues
2





masses


80
1
MAXDN
maximal electrotopological negative variation
topological descriptors
2


1012
1
L2s
2nd component size directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states


481
1
BELe3
lowest eigenvalue n. 3 of Burden matrix/weighted by atomic
Burden eigenvalues
2





Sanderson electronegativities


665
1
RDF095u
Radial Distribution Function - 9.5/unweighted
RDF descriptors
3







MOR204.6












1262
1
nCconj
number of non-aromatic conjugated C(sp2)
functional group counts
1


1463
1
S_dsCH
S_dsCH
atomtypes (cerius2)
1


1092
2
HATS3m
leverage-weighted autocorrelation of lag 3/weighted by atomic
GETAWAY descriptors
3





masses


635
1
DISPv
d COMMA2 value/weighted by atomic van der Waals volumes
geometrical descriptors
3


1174
1
R4u+
R maximal autocorrelation of lag 4/unweighted
GETAWAY descriptors
3


107
2
D/Dr06
distance/detour ring index of order 6
topological descriptors
2


1185
1
R6m
R autocorrelation of lag 6/weighted by atomic masses
GETAWAY descriptors
3


837
1
Mor10m
3D-MoRSE - signal 10/weighted by atomic masses
3D-MoRSE descriptors
3


373
1
EEig09r
Eigenvalue 09 from edge adj. matrix weighted by resonance
edge adjacency indices
2





integrals


1222
1
R7e
R autocorrelation of lag 7/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities


1173
1
R3u+
R maximal autocorrelation of lag 3/unweighted
GETAWAY descriptors
3


1199
1
R2v
R autocorrelation of lag 2/weighted by atomic van der Waals
GETAWAY descriptors
3





volumes


1371
1
F05[C—O]
frequency of C—O at topological distance 05
2D frequency fingerprints
2


1136
1
HATS7e
leverage-weighted autocorrelation of lag 7/weighted by atomic
GETAWAY descriptors
3





Sanderson electronegativities







MOR207.1












1290
3
C-025
R—CR—R
atom-centred fragments
1


1371
1
F05[C—O]
frequency of C—O at topological distance 05
2D frequency fingerprints
2


1212
1
R6v+
R maximal autocorrelation of lag 6/weighted by atomic van der
GETAWAY descriptors
3





Waals volumes


998
2
E2e
2nd component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic Sanderson electronegativities


1464
1
S_aaCH
S_aaCH
atomtypes (cerius2)
1


1233
2
RTe+
R maximal index/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities


1178
1
R8u+
R maximal autocorrelation of lag 8/unweighted
GETAWAY descriptors
3


262
1
ATS2p
Broto-Moreau autocorrelation of a topological structure - lag 2/
2D autocorrelations
2





weighted by atomic polarizabilities


297
1
MATS5p
Moran autocorrelation - lag 5/weighted by atomic polarizabilities
2D autocorrelations
2


714
1
RDF045v
Radial Distribution Function - 4.5/weighted by atomic van der
RDF descriptors
3





Waals volumes


1004
1
P2p
2nd component shape directional WHIM index/weighted by
WHIM descriptors
3





atomic polarizabilities


1249
1
R7p+
R maximal autocorrelation of lag 7/weighted by atomic
GETAWAY descriptors
3





polarizabilities


1184
1
R5m
R autocorrelation of lag 5/weighted by atomic masses
GETAWAY descriptors
3


627
1
HOMA
Harmonic Oscillator Model of Aromaticity index
geometrical descriptors
3


1213
1
R7v+
R maximal autocorrelation of lag 7/weighted by atomic van der
GETAWAY descriptors
3





Waals volumes


1140
1
H1p
H autocorrelation of lag 1/weighted by atomic polarizabilities
GETAWAY descriptors
3







MOR273.1












1015
1
P2s
2nd component shape directional WHIM index/weighted by
WHIM descriptors
3





atomic electrotopological states


77
2
Jhetv
Balaban-type index from van der Waals weighted distance matrix
topological descriptors
2


305
1
GATS5m
Geary autocorrelation - lag 5/weighted by atomic masses
2D autocorrelations
2


1070
1
HATS1u
leverage-weighted autocorrelation of lag 1/unweighted
GETAWAY descriptors
3


815
1
Mor20u
3D-MoRSE - signal 20/unweighted
3D-MoRSE descriptors
3


518
1
JGI6
mean topological charge index of order6
topological charge indices
2


1216
1
R1e
R autocorrelation of lag 1/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities


827
1
Mor32u
3D-MoRSE - signal 32/unweighted
3D-MoRSE descriptors
3


372
1
EEig08r
Eigenvalue 08 from edge adj. matrix weighted by resonance
edge adjacency indices
2





integrals


441
1
BEHm3
highest eigenvalue n. 3 of Burden matrix/weighted by atomic
Burden eigenvalues
2





masses







MOR250.1












1045
1
Dv
D total accessibility index/weighted by atomic van der Waals
WHIM descriptors
3





volumes


1297
6
H-047
H attached to C1(sp3)/C0(sp2)
atom-centred fragments
1


443
1
BEHm5
highest eigenvalue n. 5 of Burden matrix/weighted by atomic
Burden eigenvalues
2





masses


1282
3
C-006
CH2RX
atom-centred fragments
1


297
2
MATS5p
Moran autocorrelation - lag 5/weighted by atomic polarizabilities
2D autocorrelations
2


1303
1
O-057
phenol/enol/carboxyl OH
atom-centred fragments
1


107
2
D/Dr06
distance/detour ring index of order 6
topological descriptors
2


947
2
Mor24p
3D-MoRSE - signal 24/weighted by atomic polarizabilities
3D-MoRSE descriptors
3


1014
3
P1s
1st component shape directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states


356
1
EEig07d
Eigenvalue 07 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2


1249
1
R7p+
R maximal autocorrelation of lag 7/weighted by atomic
GETAWAY descriptors
3





polarizabilities


986
3
E1v
1st component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic van der Waals volumes


1012
2
L2s
2nd component size directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states


901
2
Mor10e
3D-MoRSE - signal 10/weighted by atomic Sanderson
3D-MoRSE descriptors
3





electronegativities


1100
1
H1v
H autocorrelation of lag 1/weighted by atomic van der Waals
GETAWAY descriptors
3





volumes


1183
4
R4m
R autocorrelation of lag 4/weighted by atomic masses
GETAWAY descriptors
3


683
1
RDF035m
Radial Distribution Function - 3.5/weighted by atomic masses
RDF descriptors
3


447
1
BELm1
lowest eigenvalue n. 1 of Burden matrix/weighted by atomic
Burden eigenvalues
2





masses


1096
2
HATS7m
leverage-weighted autocorrelation of lag 7/weighted by atomic
GETAWAY descriptors
3





masses


1367
1
F04[C—O]
frequency of C—O at topological distance 04
2D frequency fingerprints
2


1336
1
B04[O—O]
presence/absence of O—O at topological distance 04
2D binary fingerprints
2


1337
1
B05[C—C]
presence/absence of C—C at topological distance 05
2D binary fingerprints
2


1280
2
C-003
CHR3
atom-centred fragments
1


1140
3
H1p
H autocorrelation of lag 1/weighted by atomic polarizabilities
GETAWAY descriptors
3


838
2
Mor11m
3D-MoRSE - signal 11/weighted by atomic masses
3D-MoRSE descriptors
3


341
1
EEig07x
Eigenvalue 07 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2


1316
3
GVWAI-80
Ghose-Viswanadhan-Wendoloski drug-like index at 80%
molecular properties
1


519
2
JGI7
mean topological charge index of order7
topological charge indices
2


147
3
piPC07
molecular multiple path count of order 07
walk and path counts
2


30
1
nR09
number of 9-membered rings
constitutional descriptors
0


776
1
RDF060p
Radial Distribution Function - 6.0/weighted by atomic
RDF descriptors
3





polarizabilities


1266
1
nRCOOH
number of carboxylic acids (aliphatic)
functional group counts
1


837
1
Mor10m
3D-MoRSE - signal 10/weighted by atomic masses
3D-MoRSE descriptors
3


302
1
GATS2m
Geary autocorrelation - lag 2/weighted by atomic masses
2D autocorrelations
2


479
2
BELe1
lowest eigenvalue n. 1 of Burden matrix/weighted by atomic
Burden eigenvalues
2





Sanderson electronegativities


212
1
IC1
information content index (neighborhood symmetry of 1-order)
information indices
2


272
1
MATS4m
Moran autocorrelation - lag 4/weighted by atomic masses
2D autocorrelations
2


1274
1
nArOR
number of ethers (aromatic)
functional group counts
1


106
1
D/Dr05
distance/detour ring index of order 5
topological descriptors
2


658
1
RDF060u
Radial Distribution Function - 6.0/unweighted
RDF descriptors
3







MOR256.17












1452
7
BIC
BIC
topological (cerius2)
2


335
1
EEig01x
Eigenvalue 01 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2


1095
6
HATS6m
leverage-weighted autocorrelation of lag 6/weighted by atomic
GETAWAY descriptors
3





masses


1272
5
nOHp
number of primary alcohols
functional group counts
1


1465
3
S_sssCH
S_sssCH
atomtypes (cerius2)
1


1270
3
nArCO
number of ketones (aromatic)
functional group counts
1


1298
3
H-049
H attached to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp)
atom-centred fragments
1


1265
3
nR = Ct
number of aliphatic tertiary C(sp2)
functional group counts
1


1088
3
HTm
H total index/weighted by atomic masses
GETAWAY descriptors
3


889
1
Mor30v
3D-MoRSE - signal 30/weighted by atomic van der Waals
3D-MoRSE descriptors
3





volumes


306
2
GATS6m
Geary autocorrelation - lag 6/weighted by atomic masses
2D autocorrelations
2


702
1
RDF130m
Radial Distribution Function - 13.0/weighted by atomic masses
RDF descriptors
3


742
2
RDF040e
Radial Distribution Function - 4.0/weighted by atomic Sanderson
RDF descriptors
3





electronegativities


31
1
nR10
number of 10-membered rings
constitutional descriptors
0


1351
1
B09[C—S]
presence/absence of C—S at topological distance 09
2D binary fingerprints
2


1283
1
C-008
CHR2X
atom-centred fragments
1


168
1
X5A
average connectivity index chi-5
connectivity indices
2


275
1
MATS7m
Moran autocorrelation - lag 7/weighted by atomic masses
2D autocorrelations
2


883
1
Mor24v
3D-MoRSE - signal 24/weighted by atomic van der Waals
3D-MoRSE descriptors
3





volumes


918
1
Mor27e
3D-MoRSE - signal 27/weighted by atomic Sanderson
3D-MoRSE descriptors
3





electronegativities


358
1
EEig09d
Eigenvalue 09 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2







MOR258.1












1198
3
R1v
R autocorrelation of lag 1/weighted by atomic van der Waals
GETAWAY descriptors
3





volumes


448
1
BELm2
lowest eigenvalue n. 2 of Burden matrix/weighted by atomic
Burden eigenvalues
2





masses


1140
2
H1p
H autocorrelation of lag 1/weighted by atomic polarizabilities
GETAWAY descriptors
3


964
1
E1u
1st component accessibility directional WHIM index/unweighted
WHIM descriptors
3


1091
1
HATS2m
leverage-weighted autocorrelation of lag 2/weighted by atomic
GETAWAY descriptors
3





masses


514
1
JGI2
mean topological charge index of order2
topological charge indices
2


1234
1
R1p
R autocorrelation of lag 1/weighted by atomic polarizabilities
GETAWAY descriptors
3


1340
1
B06[C—C]
presence/absence of C—C at topological distance 06
2D binary fingerprints
2


1012
1
L2s
2nd component size directional WHIM index/weighted by atomic
WHIM descriptors
3





electrotopological states


631
1
DISPm
d COMMA2 value/weighted by atomic masses
geometrical descriptors
3


608
1
SHP2
average shape profile index of order 2
Randic molecular profiles
3


1060
1
H1u
H autocorrelation of lag 1/unweighted
GETAWAY descriptors
3


1015
1
P2s
2nd component shape directional WHIM index/weighted by
WHIM descriptors
3





atomic electrotopological states







MOR259.1












1261
1
nCb−
number of substituted benzene C(sp2)
functional group counts
1


1018
1
G3s
3st component symmetry directional WHIM index/weighted by
WHIM descriptors
3





atomic electrotopological states


1183
1
R4m
R autocorrelation of lag 4/weighted by atomic masses
GETAWAY descriptors
3


136
1
MPC06
molecular path count of order 06
walk and path counts
2


635
1
DISPv
d COMMA2 value/weighted by atomic van der Waals volumes
geometrical descriptors
3


1234
1
R1p
R autocorrelation of lag 1/weighted by atomic polarizabilities
GETAWAY descriptors
3


1371
1
F05[C—O]
frequency of C—O at topological distance 05
2D frequency fingerprints
2


1208
1
R2v+
R maximal autocorrelation of lag 2/weighted by atomic van der
GETAWAY descriptors
3





Waals volumes


964
1
E1u
1st component accessibility directional WHIM index/unweighted
WHIM descriptors
3


302
1
GATS2m
Geary autocorrelation - lag 2/weighted by atomic masses
2D autocorrelations
2


998
1
E2e
2nd component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic Sanderson electronegativities


1060
1
H1u
H autocorrelation of lag 1/unweighted
GETAWAY descriptors
3







MOR260.1












727
1
RDF110v
Radial Distribution Function - 11.0/weighted by atomic van der
RDF descriptors
3





Waals volumes


1190
2
R2m+
R maximal autocorrelation of lag 2/weighted by atomic masses
GETAWAY descriptors
3


520
1
JGI8
mean topological charge index of order8
topological charge indices
2


1308
1
Hy
hydrophilic factor
molecular properties
1


1302
1
O-056
alcohol
atom-centred fragments
1


1299
1
H-050
H attached to heteroatom
atom-centred fragments
1


276
1
MATS8m
Moran autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2


750
1
RDF080e
Radial Distribution Function - 8.0/weighted by atomic Sanderson
RDF descriptors
3





electronegativities


1095
1
HATS6m
leverage-weighted autocorrelation of lag 6/weighted by atomic
GETAWAY descriptors
3





masses







MOR261.1












756
1
RDF110e
Radial Distribution Function - 11.0/weighted by atomic Sanderson
RDF descriptors
3





electronegativities


1282
2
C-006
CH2RX
atom-centred fragments
1


720
1
RDF075v
Radial Distribution Function - 7.5/weighted by atomic van der
RDF descriptors
3





Waals volumes


665
1
RDF095u
Radial Distribution Function - 9.5/unweighted
RDF descriptors
3


631
1
DISPm
d COMMA2 value/weighted by atomic masses
geometrical descriptors
3


1278
1
C-001
CH3R/CH4
atom-centred fragments
1


446
1
BEHm8
highest eigenvalue n. 8 of Burden matrix/weighted by atomic
Burden eigenvalues
2





masses


727
1
RDF110v
Radial Distribution Function - 11.0/weighted by atomic van der
RDF descriptors
3





Waals volumes







MOR268.1












260
2
ATS8e
Broto-Moreau autocorrelation of a topological structure - lag 8/
2D autocorrelations
2





weighted by atomic Sanderson electronegativities


1282
1
C-006
CH2RX
atom-centred fragments
1


83
1
TIE
E-state topological parameter
topological descriptors
2


686
1
RDF050m
Radial Distribution Function - 5.0/weighted by atomic masses
RDF descriptors
3


1350
3
B09[C—O]
presence/absence of C—O at topological distance 09
2D binary fingerprints
2


1343
5
B07[C—C]
presence/absence of C—C at topological distance 07
2D binary fingerprints
2


1300
4
H-051
H attached to alpha-C
atom-centred fragments
1


1465
3
S_sssCH
S_sssCH
atomtypes (cerius2)
1


274
1
MATS6m
Moran autocorrelation - lag 6/weighted by atomic masses
2D autocorrelations
2


1006
2
G2p
2st component symmetry directional WHIM index/weighted by
WHIM descriptors
3





atomic polarizabilities


757
1
RDF115e
Radial Distribution Function - 11.5/weighted by atomic Sanderson
RDF descriptors
3





electronegativities


1298
1
H-049
H attached to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp)
atom-centred fragments
1


1303
1
O-057
phenol/enol/carboxyl OH
atom-centred fragments
1


672
1
RDF130u
Radial Distribution Function - 13.0/unweighted
RDF descriptors
3


963
1
G3u
3st component symmetry directional WHIM index/unweighted
WHIM descriptors
3


1268
1
nRCHO
number of aldehydes (aliphatic)
functional group counts
1


1270
1
nArCO
number of ketones (aromatic)
functional group counts
1


1266
1
nRCOOH
number of carboxylic acids (aliphatic)
functional group counts
1


301
1
GATS1m
Geary autocorrelation - lag 1/weighted by atomic masses
2D autocorrelations
2


1262
1
nCconj
number of non-aromatic conjugated C(sp2)
functional group counts
1


297
1
MATS5p
Moran autocorrelation - lag 5/weighted by atomic polarizabilities
2D autocorrelations
2







MOR271.1












1299
1
H-050
H attached to heteroatom
atom-centred fragments
1


88
1
PHI
Kier flexibility index
topological descriptors
2


518
3
JGI6
mean topological charge index of order6
topological charge indices
2


691
2
RDF075m
Radial Distribution Function - 7.5/weighted by atomic masses
RDF descriptors
3


1298
2
H-049
H attached to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp)
atom-centred fragments
1


621
1
SPH
spherosity
geometrical descriptors
3


1308
2
Hy
hydrophilic factor
molecular properties
1


343
1
EEig09x
Eigenvalue 09 from edge adj. matrix weighted by edge degrees
edge adjacency indices
2


1179
1
RTu+
R maximal index/unweighted
GETAWAY descriptors
3


308
1
GATS8m
Geary autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2


1266
1
nRCOOH
number of carboxylic acids (aliphatic)
functional group counts
1


786
1
RDF110p
Radial Distribution Function - 11.0/weighted by atomic
RDF descriptors
3





polarizabilities


304
1
GATS4m
Geary autocorrelation - lag 4/weighted by atomic masses
2D autocorrelations
2


297
1
MATS5p
Moran autocorrelation - lag 5/weighted by atomic polarizabilities
2D autocorrelations
2


1196
1
R8m+
R maximal autocorrelation of lag 8/weighted by atomic masses
GETAWAY descriptors
3







MOR272.1












1322
1
BLTF96
Verhaar model of Fish base-line toxicity from MLOGP (mmol/l)
molecular properties
1


639
1
DISPe
d COMMA2 value/weighted by atomic Sanderson
geometrical descriptors
3





electronegativities


1347
1
B08[C—O]
presence/absence of C—O at topological distance 08
2D binary fingerprints
2


1155
1
HATS6p
leverage-weighted autocorrelation of lag 6/weighted by atomic
GETAWAY descriptors
3





polarizabilities


274
1
MATS6m
Moran autocorrelation - lag 6/weighted by atomic masses
2D autocorrelations
2


727
1
RDF110v
Radial Distribution Function - 11.0/weighted by atomic van der
RDF descriptors
3





Waals volumes


1018
1
G3s
3st component symmetry directional WHIM index/weighted by
WHIM descriptors
3





atomic electrotopological states


1298
1
H-049
H attached to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp)
atom-centred fragments
1


1299
1
H-050
H attached to heteroatom
atom-centred fragments
1


1190
1
R2m+
R maximal autocorrelation of lag 2/weighted by atomic masses
GETAWAY descriptors
3


308
1
GATS8m
Geary autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2


1134
1
HATS5e
leverage-weighted autocorrelation of lag 5/weighted by atomic
GETAWAY descriptors
3





Sanderson electronegativities


1082
1
H3m
H autocorrelation of lag 3/weighted by atomic masses
GETAWAY descriptors
3


441
1
BEHm3
highest eigenvalue n. 3 of Burden matrix/weighted by atomic
Burden eigenvalues
2





masses







MOR273.1












1015
1
P2s
2nd component shape directional WHIM index/weighted by
WHIM descriptors
3





atomic electrotopological states


77
2
Jhetv
Balaban-type index from van der Waals weighted distance matrix
topological descriptors
2


305
1
GATS5m
Geary autocorrelation - lag 5/weighted by atomic masses
2D autocorrelations
2


1070
1
HATS1u
leverage-weighted autocorrelation of lag 1/unweighted
GETAWAY descriptors
3


815
1
Mor20u
3D-MoRSE - signal 20/unweighted
3D-MoRSE descriptors
3


518
1
JGI6
mean topological charge index of order6
topological charge indices
2


1216
1
R1e
R autocorrelation of lag 1/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities


827
1
Mor32u
3D-MoRSE - signal 32/unweighted
3D-MoRSE descriptors
3


372
1
EEig08r
Eigenvalue 08 from edge adj. matrix weighted by resonance
edge adjacency indices
2





integrals


441
1
BEHm3
highest eigenvalue n. 3 of Burden matrix/weighted by atomic
Burden eigenvalues
2





masses







MOR277.1












1112
1
HATS3v
leverage-weighted autocorrelation of lag 3/weighted by atomic
GETAWAY descriptors
3





van der Waals volumes


997
4
E1e
1st component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic Sanderson electronegativities


273
1
MATS5m
Moran autocorrelation - lag 5/weighted by atomic masses
2D autocorrelations
2


1009
1
E2p
2nd component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic polarizabilities


683
2
RDF035m
Radial Distribution Function - 3.5/weighted by atomic masses
RDF descriptors
3


1190
2
R2m+
R maximal autocorrelation of lag 2/weighted by atomic masses
GETAWAY descriptors
3


1232
1
R8e+
R maximal autocorrelation of lag 8/weighted by atomic
GETAWAY descriptors
3





Sanderson electronegativities


608
3
SHP2
average shape profile index of order 2
Randic molecular profiles
3


306
1
GATS6m
Geary autocorrelation - lag 6/weighted by atomic masses
2D autocorrelations
2


497
1
BELp3
lowest eigenvalue n. 3 of Burden matrix/weighted by atomic
Burden eigenvalues
2





polarizabilities


79
1
Jhetp
Balaban-type index from polarizability weighted distance matrix
topological descriptors
2


1300
1
H-051
H attached to alpha-C
atom-centred fragments
1


373
1
EEig09r
Eigenvalue 09 from edge adj. matrix weighted by resonance
edge adjacency indices
2





integrals


481
1
BELe3
lowest eigenvalue n. 3 of Burden matrix/weighted by atomic
Burden eigenvalues
2





Sanderson electronegativities


1267
1
nRCOOR
number of esters (aliphatic)
functional group counts
1


965
1
E2u
2nd component accessibility directional WHIM index/unweighted
WHIM descriptors
3


517
1
JGI5
mean topological charge index of order5
topological charge indices
2


303
1
GATS3m
Geary autocorrelation - lag 3/weighted by atomic masses
2D autocorrelations
2


957
1
L2u
2nd component size directional WHIM index/unweighted
WHIM descriptors
3


1466
1
S_dssC
S_dssC
atomtypes (cerius2)
1


996
1
G3e
3st component symmetry directional WHIM index/weighted by
WHIM descriptors
3





atomic Sanderson electronegativities


1340
1
B06[C—C]
presence/absence of C—C at topological distance 06
2D binary fingerprints
2


1001
1
L2p
2nd component size directional WHIM index/weighted by atomic
WHIM descriptors
3





polarizabilities







MOR30.1












1350
1
B09[C—O]
presence/absence of C—O at topological distance 09
2D binary fingerprints
2


1302
1
O-056
alcohol
atom-centred fragments
1


1344
2
B07[C—O]
presence/absence of C—O at topological distance 07
2D binary fingerprints
2


722
1
RDF085v
Radial Distribution Function - 8.5/weighted by atomic van der
RDF descriptors
3





Waals volumes


1300
5
H-051
H attached to alpha-C
atom-centred fragments
1


691
1
RDF075m
Radial Distribution Function - 7.5/weighted by atomic masses
RDF descriptors
3


1282
3
C-006
CH2RX
atom-centred fragments
1


625
1
L/Bw
length-to-breadth ratio by WHIM
geometrical descriptors
3


356
1
EEig07d
Eigenvalue 07 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2


724
1
RDF095v
Radial Distribution Function - 9.5/weighted by atomic van der
RDF descriptors
3





Waals volumes


1009
1
E2p
2nd component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic polarizabilities


307
2
GATS7m
Geary autocorrelation - lag 7/weighted by atomic masses
2D autocorrelations
2


857
1
Mor30m
3D-MoRSE - signal 30/weighted by atomic masses
3D-MoRSE descriptors
3


804
1
Mor09u
3D-MoRSE - signal 09/unweighted
3D-MoRSE descriptors
3


355
1
EEig06d
Eigenvalue 06 from edge adj. matrix weighted by dipole moments
edge adjacency indices
2


308
1
GATS8m
Geary autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2


1321
1
Infective-80
Ghose-Viswanadhan-Wendoloski antiinfective-like index at 80%
molecular properties
1


1230
1
R6e+
R maximal autocorrelation of lag 6/weighted by atomic
GETAWAY descriptors
3





Sanderson electronegativities


302
1
GATS2m
Geary autocorrelation - lag 2/weighted by atomic masses
2D autocorrelations
2


743
1
RDF045e
Radial Distribution Function - 4.5/weighted by atomic Sanderson
RDF descriptors
3





electronegativities







MOR33.1












1377
1
F07[C—O]
frequency of C—O at topological distance 07
2D frequency fingerprints
2


1266
1
nRCOOH
number of carboxylic acids (aliphatic)
functional group counts
1


635
1
DISPv
d COMMA2 value/weighted by atomic van der Waals volumes
geometrical descriptors
3


1367
1
F04[C—O]
frequency of C—O at topological distance 04
2D frequency fingerprints
2


908
2
Mor17e
3D-MoRSE - signal 17/weighted by atomic Sanderson
3D-MoRSE descriptors
3





electronegativities


1300
1
H-051
H attached to alpha-C
atom-centred fragments
1


1282
1
C-006
CH2RX
atom-centred fragments
1


307
1
GATS7m
Geary autocorrelation - lag 7/weighted by atomic masses
2D autocorrelations
2


1299
1
H-050
H attached to heteroatom
atom-centred fragments
1







MOR37.1












1350
1
B09[C—O]
presence/absence of C—O at topological distance 09
2D binary fingerprints
2


1302
1
O-056
alcohol
atom-centred fragments
1


1347
1
B08[C—O]
presence/absence of C—O at topological distance 08
2D binary fingerprints
2







MOR40.1












727
2
RDF110v
Radial Distribution Function - 11.0/weighted by atomic van der
RDF descriptors
3





Waals volumes


1300
1
H-051
H attached to alpha-C
atom-centred fragments
1


908
1
Mor17e
3D-MoRSE - signal 17/weighted by atomic Sanderson
3D-MoRSE descriptors
3





electronegativities


1282
1
C-006
CH2RX
atom-centred fragments
1


307
1
GATS7m
Geary autocorrelation - lag 7/weighted by atomic masses
2D autocorrelations
2







MOR41.1












201
1
HVcpx
graph vertex complexity index
information indices
2


1443
1
Kappa-3
Kappa-3
topological (cerius2)
2


303
4
GATS3m
Geary autocorrelation - lag 3/weighted by atomic masses
2D autocorrelations
2


1266
8
nRCOOH
number of carboxylic acids (aliphatic)
functional group counts
1


1298
3
H-049
H attached to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp)
atom-centred fragments
1


869
2
Mor10v
3D-MoRSE - signal 10/weighted by atomic van der Waals
3D-MoRSE descriptors
3





volumes


781
2
RDF085p
Radial Distribution Function - 8.5/weighted by atomic
RDF descriptors
3





polarizabilities


372
3
EEig08r
Eigenvalue 08 from edge adj. matrix weighted by resonance
edge adjacency indices
2





integrals


1452
4
BIC
BIC
topological (cerius2)
2


308
5
GATS8m
Geary autocorrelation - lag 8/weighted by atomic masses
2D autocorrelations
2


1085
3
H6m
H autocorrelation of lag 6/weighted by atomic masses
GETAWAY descriptors
3


489
1
BEHp3
highest eigenvalue n. 3 of Burden matrix/weighted by atomic
Burden eigenvalues
2





polarizabilities


515
1
JGI3
mean topological charge index of order3
topological charge indices
2


663
4
RDF085u
Radial Distribution Function - 8.5/unweighted
RDF descriptors
3


302
1
GATS2m
Geary autocorrelation - lag 2/weighted by atomic masses
2D autocorrelations
2


913
4
Mor22e
3D-MoRSE - signal 22/weighted by atomic Sanderson
3D-MoRSE descriptors
3





electronegativities


1255
3
nCq
number of total quaternary C(sp3)
functional group counts
1


1008
1
E1p
1st component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic polarizabilities


715
1
RDF050v
Radial Distribution Function - 5.0/weighted by atomic van der
RDF descriptors
3





Waals volumes


91
2
PW3
path/walk 3-Randic shape index
topological descriptors
2


1316
1
GVWAI-80
Ghose-Viswanadhan-Wendoloski drug-like index at 80%
molecular properties
1


1283
1
C-008
CHR2X
atom-centred fragments
1


1105
1
H6v
H autocorrelation of lag 6/weighted by atomic van der Waals
GETAWAY descriptors
3





volumes


271
1
MATS3m
Moran autocorrelation - lag 3/weighted by atomic masses
2D autocorrelations
2


1405
1
Atype_C_18
Number of Carbon Type 18
atomtypes (Cerius2)
1


457
1
BEHv3
highest eigenvalue n. 3 of Burden matrix/weighted by atomic van
Burden eigenvalues
2





der Waals volumes


672
1
RDF130u
Radial Distribution Function - 13.0/unweighted
RDF descriptors
3


1268
1
nRCHO
number of aldehydes (aliphatic)
functional group counts
1


1338
1
B05[C—O]
presence/absence of C—O at topological distance 05
2D binary fingerprints
2


620
1
MEcc
molecular eccentricity
geometrical descriptors
3


165
1
X2A
average connectivity index chi-2
connectivity indices
2







MOR5.1












1266
1
nRCOOH
number of carboxylic acids (aliphatic)
functional group counts
1


1377
1
F07[C—O]
frequency of C—O at topological distance 07
2D frequency fingerprints
2


1367
1
F04[C—O]
frequency of C—O at topological distance 04
2D frequency fingerprints
2


1303
1
O-057
phenol/enol/carboxyl OH
atom-centred fragments
1


908
1
Mor17e
3D-MoRSE - signal 17/weighted by atomic Sanderson
3D-MoRSE descriptors
3





electronegativities







OR1A1












1077
2
HATS8u
leverage-weighted autocorrelation of lag 8/unweighted
GETAWAY descriptors
3


1019
1
E1s
1st component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic electrotopological states


1211
2
R5v+
R maximal autocorrelation of lag 5/weighted by atomic van der
GETAWAY descriptors
3





Waals volumes


925
1
Mor02p
3D-MoRSE - signal 02/weighted by atomic polarizabilities
3D-MoRSE descriptors
3


639
1
DISPe
d COMMA2 value/weighted by atomic Sanderson
geometrical descriptors
3





electronegativities


1340
3
B06[C—C]
presence/absence of C—C at topological distance 06
2D binary fingerprints
2


1268
2
nRCHO
number of aldehydes (aliphatic)
functional group counts
1


944
1
Mor21p
3D-MoRSE - signal 21/weighted by atomic. polarizabilities
3D-MoRSE descriptors
3


515
1
JGI3
mean topological charge index of order3
topological charge indices
2


1303
1
O-057
phenol/enol/carboxyl OH
atom-centred fragments
1


696
1
RDF100m
Radial Distribution Function - 10.0/weighted by atomic masses
RDF descriptors
3


273
1
MATS5m
Moran autocorrelation - lag 5/weighted by atomic masses
2D autocorrelations
2


1194
1
R6m+
R maximal autocorrelation of lag 6/weighted by atomic masses
GETAWAY descriptors
3


665
1
RDF095u
Radial Distribution Function - 9.5/unweighted
RDF descriptors
3


1266
1
nRCOOH
number of carboxylic acids (aliphatic)
functional group counts
1


414
1
ESpm06d
Spectral moment 06 from edge adj. matrix weighted by dipole
edge adjacency indices
2





moments


451
1
BELm5
lowest eigenvalue n. 5 of Burden matrix/weighted by atomic
Burden eigenvalues
2





masses







OR2J2












1019
3
E1s
1st component accessibility directional WHIM index/weighted by
WHIM descriptors
3





atomic electrotopological states


1374
1
F06[C—O]
frequency of C—O at topological distance 06
2D frequency fingerprints
2


635
1
DISPv
d COMMA2 value/weighted by atomic van der Weals volumes
geometrical descriptors
3


517
1
JGI5
mean topological charge index of order5
topological charge indices
2


1300
3
H-051
H attached to alpha-C
atom-centred fragments
1


1060
1
H1u
H autocorrelation of lag 1/unweighted
GETAWAY descriptors
3


631
4
DISPm
d COMMA2 value/weighted by atomic masses
geometrical descriptors
3


462
1
BEHv8
highest eigenvalue n. 8 of Burden matrix/weighted by atomic van
Burden eigenvalues
2





der Weals volumes


1298
2
H-049
H attached to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp)
atom-centred fragments
1


1341
1
B06[C—O]
presence/absence of C—O at topological distance 06
2D binary fingerprints
2


1303
1
O-057
phenol/enol/carboxyl OH
atom-centred fragments
1


805
1
Mor10u
3D-MoRSE - signal 10/unweighted
3D-MoRSE descriptors
3


1087
1
H8m
H autocorrelation of lag 8/weighted by atomic masses
GETAWAY descriptors
3


1355
2
F01[C—C]
frequency of C—C at topological distance 01
2D frequency fingerprints
2


1154
2
HATS5p
leverage-weighted autocorrelation of lag 5/weighted by atomic
GETAWAY descriptors
3





polarizabilities


297
1
MATS5p
Moran autocorrelation - lag 5/weighted by atomic polarizabilities
2D autocorrelations
2


1085
1
H6m
H autocorrelation of lag 6/weighted by atomic masses
GETAWAY descriptors
3


1466
1
S_dssC
S_dssC
atomtypes (cerius2)
1


1129
1
HATS0e
leverage-weighted autocorrelation of lag 0/weighted by atomic
GETAWAY descriptors
3





Sanderson electronegativities


1249
1
R7p+
R maximal autocorrelation of lag 7/weighted by atomic
GETAWAY descriptors
3





polarizabilities


541
1
VEA2
average eigenvector coefficient sum from adjacency matrix
eigenvalue-based indices
2







OR2W1












1337
2
B05[C—C]
presence/absence of C—C at topological distance 05
2D binary fingerprints
2


1155
2
HATS6p
leverage-weighted autocorrelation of lag 6/weighted by atomic
GETAWAY descriptors
3





polarizabilities


698
1
RDF110m
Radial Distribution Function - 11.0/weighted by atomic masses
RDF descriptors
3


1190
1
R2m+
R maximal autocorrelation of lag 2/weighted by atomic masses
GETAWAY descriptors
3


297
1
MATS5p
Moran autocorrelation - lag 5/weighted by atomic polarizabilities
2D autocorrelations
2







OR5P3












1262
2
nCconj
number of non-aromatic conjugated C(sp2)
functional group counts
1


1092
1
HATS3m
leverage-weighted autocorrelation of lag 3/weighted by atomic
GETAWAY descriptors
3





masses


1222
3
R7e
R autocorrelation of lag 7/weighted by atomic Sanderson
GETAWAY descriptors
3





electronegativities


206
1
Yindex
Balaban Y index
information indices
2


1231
1
R7e+
R maximal autocorrelation of lag 7/weighted by atomic
GETAWAY descriptors
3





Sanderson electronegativities


1323
2
BLTD48
Verhaar model of Daphnia base-line toxicity from MLOGP (mmol/l)
molecular properties
1


1185
1
R6m
R autocorrelation of lag 6/weighted by atomic masses
GETAWAY descriptors
3


1297
1
H-047
H attached to C1(sp3)/C0(sp2)
atom-centred fragments
1


1183
1
R4m
R autocorrelation of lag 4/weighted by atomic masses
GETAWAY descriptors
3


302
3
GATS2m
Geary autocorrelation - lag 2/weighted by atomic masses
2D autocorrelations
2


631
1
DISPm
d COMMA2 value/weighted by atomic masses
geometrical descriptors
3


805
2
Mor10u
3D-MoRSE - signal 10/unweighted
3D-MoRSE descriptors
3


774
1
RDF050p
Radial Distribution Function - 5.0/weighted by atomic
RDF descriptors
3





polarizabilities


1336
1
B04[O—O]
presence/absence of O—O at topological distance 04
2D binary fingerprints
2


447
1
BELm1
lowest eigenvalue n. 1 of Burden matrix/weighted by atomic
Burden eigenvalues
2





masses


870
1
Mor11v
3D-MoRSE - signal 11/weighted by atomic van der Waals
3D-MoRSE descriptors
3





volumes


1136
2
HATS7e
leverage-weighted autocorrelation of lag 7/weighted by atomic
GETAWAY descriptors
3





Sanderson electronegativities


1337
1
B05[C—C]
presence/absence of C—C at topological distance 05
2D binary fingerprints
2


1298
1
H-049
H attached to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp)
atom-centred fragments
1


1343
2
B07[C—C]
presence/absence of C—C at topological distance 07
2D binary fingerprints
2


1266
1
nRCOOH
number of carboxylic acids (aliphatic)
functional group counts
1


941
1
Mor18p
3D-MoRSE - signal 18/weighted by atomic polarizabilities
3D-MoRSE descriptors
3


1111
1
HATS2v
leverage-weighted autocorrelation of lag 2/weighted by atomic
GETAWAY descriptors
3





van der Waals volumes
















TABLE 6







Top ~25 predicted compounds for each Mammalian OR. Tables contain


SMILES strings, and distances, of the top ~25 predicted compounds for


each Or. All distances represent the minimum distance based on optimized


descriptors to an active compound listed in gray cells for that particular Or.








SMILES
Distance










Mor1-1








CC1═CC2═C(C═C1)OC(═O)C2
0.04917087


CC1═CC2═C(CC(═O)O2)C═C1
0.06445035


CC1═CC═CC2═C1OC(═O)C2
0.06478577


CC(CCCC(═O)O)N
0.0766186


CC1═CC(═C2C(═C1)CC(═O)O2)C
0.09134395


CC1═C(C2═C(CC(═O)O2)C═C1)C
0.09749545


CC1═CC(═C2CC(═O)OC2═C1)C
0.1021952


CC(C)(C)C(CCC(═O)O)O
0.1026351


C1C2═C(C(═CC═C2)N)OC1═O
0.1122016


C1C2═CC═CC═C2NC1═O
0.1200522


C1C2═C(C═CC═C2OC1═O)N
0.1221153


C═CCCCC(═O)O
0.1245319


CC(C)(C)CCCC(═O)N
0.1311838


C(CC(N)N)CC(═O)O
0.1339592


CC(C1═C2CC(═O)NC2═CC═C1)N
0.1356993


CC1═C2CC(═O)OC2═CC═C1
0.136907


CC1═CC═CC2═C1NC(═O)C2
0.1403033


C(═O)CCCC
0.1404621


C1C2═C(C═C(C═C2)O)OC1═O
0.1431714


CCC(C)(C)CC(═O)N
0.1436566


CC(CCCC(═O)O)O
0.1452177


C1C2═C(C═C(C═C2)N)NC1═O
0.146264


CC(C)(C)C1═CCC(═O)O1
0.1538394


CCCCCC(═O)N
0.1546248


CC(C)(C)CC(CC(═O)O)O
0.156154


C1═CNC═C1CCC(═O)N
0.1571392







Mor106-1








CC═COC1═CC═CC═C1
0.08891955


CC(S)SC1═CC═CC═C1
0.1534956


C═C(C1═CC═CC═C1)C
0.1583203


CC(═C)C1═CC═CC═C1
0.1583203


CC1═C2C(═CC═C1)N2C
0.1611889


CC1═CC═CC═C1C═C
0.1622158


CC1C(N1)C2═CC═CC═C2
0.1766318


CSC(═O)C1═CC═CC═C1
0.1913783


CSC(C1═CC═CC═C1)S
0.1981317


C═COC1═CC═CC(═C1)O
0.1996824


C═CC1═CC(═C(C═C1)S)S
0.2060344


C═CC1═CC(═CC═C1)N═C═S
0.2098719


C#COC1═CC═CC═C1
0.2130395


C1CC1CC2═CC═CC═C2
0.2152684


COC═CC1═CC═CC═C1
0.2170264


CC1═C(C2═C(O2)C═C1)C
0.2178679


c1(ccccc1)CC#N
0.2180725


CC1═CC═CC═C1OC#C
0.2181859


C═COC1═CC═CC═C1
0.2188387


CSC1═CC═CC2═C1C═C2
0.219095


C1═CC═C(C═C1)C(═O)NO
0.2207775


C1═CC═C(C═C1)N(C(═S)S)O
0.2218528


C1═CC═C2C(═C1)C(═O)SC2═O
0.2247712


C1═CC═C(C═C1)S(═O)(═O)N═C═S
0.226246


CCC1═C2C(═CC═C1)N2
0.2282767


C1═CC═C(C═C1)C2OS2(═O)═O
0.2323088







Mor107-1








CC1(C2(CCC1(CC2═O)N)C)C
0.2473219


CC1C2(CCC1(C(═O)C2)C)C
0.2764237


CC1(C2(CCC1(CC2═O)O)C)C
0.3186515


CC1(C2CC(C1(C(═O)C2)C)O)C
0.3204451


CC1(C2CC(═O)C1(CC2O)C)C
0.3482935


CC1(C2CCC1(C(═O)C2)CO)C
0.366073


CC1(C2CCC1(C(═O)C2)CS)C
0.4426886


CCC12CCC(C1(C)C)CC2═O
0.4550365


CC12CCC(C1(C)CO)CC2═O
0.4566952


CC1(C2CCC1(CC2═O)C)C
0.4653999


CC1(C2CCC1(C(C2═O)O)C)C
0.4703974


CC1(C2CC(C1(CC2═O)C)O)C
0.5505192


CC(C)C12CCC(C1)(CC2═O)C
0.5732307


CC1(C2CCC1(C(═NC)C2)C)C
0.5802225


CC1(C(CC2C1(C2)C)CC═O)C
0.6171489


CC1C2CCC(C1═O)(C2(C)C)C
0.629438


CC1(C2CCC1(C(═O)C2)C═C)C
0.6401904


CC1(C2(CCC1(CC2═O)OC)C)C
0.6463336


CCCC1(CCC(C(═O)C1)(C)C)C
0.6494132


CC1(CCCC12CC═NC2)C
0.6903515


CC1(C2CCC1(C(═O)C2)C═O)C
0.7002376


CC1(C(C1(C)C)C(═O)NC2CC2)C
0.7104875


CC12CC3C1(C(═O)CC2C3)C
0.7142688


CCOC(═O)C1C2(C13CC3)CC2
0.7263507


CC12CCCC(═O)C1(COC2)C
0.7281732


CCC12CCC1C(CC2═O)(C)C
0.7489101







Mor129-1








C1C(═O)CNC2═CC═CC═C21
0.1220788


CC1═CCCC(C1)(C)C(═O)C
0.131721


C1C2C(═CC═CO2)C═CC1═O
0.1324226


CC1CC(CC═C1C#N)(C)C
0.1401867


CC1═CCC(CC1)C(C)C═O
0.1440427


C1CCC2C(C1)CCC(═O)O2
0.1447183


CC(C)(CO)C1═CC═CC═C1
0.1488985


C1═CC═C2C(═C1)C═COC2═O
0.1541709


COC12CCC(CC1)NC(═O)C2
0.1556671


C1═CC═C2C(═C1)C═CC(═O)N2
0.1562217


CC(═O)C1═CC═CC═C1N
0.1588058


CC1CC(═CC(C1CO)C)C
0.1637881


CC1CCC(═CC1═O)C(C)C
0.1638838


C1═CC═C2C(═C1)C(═O)C═CN2
0.1644921


CC1CC(CC═C1C═O)(C)C
0.1653725


C1═CNC2═CC(═O)C═CC2═C1
0.168101


C1═CC═C2C(═C1)C(═O)C═CO2
0.1688119


C1C═CC2═C(C1O)N═CC═C2
0.1727835


CC1═CCC(CC1O)C(═C)C
0.1737252


C1C2═CC═CC═C2C(═O)CN1
0.1787667


CC1CCC(CC1C)(C)C═O
0.1795582


C1CC(CC═C1)CC#N
0.1819388


CC(C)(C)C1═CC═C(CC1)O
0.1839154


c1(ccccc1)C(C)O
0.1866107


C1C(═O)C═C2C═CC═CC2═N1
0.1886765


C1CC═CC(C1)CC═O
0.1891273







Mor136-1








CCC1(CC(OC1═N)(C)C)CC
0.05816986


C1═CC(═C2C(═C1)S2)C(═S)N
0.06587855


CC(C)(C)C1CC(═O)C2C1C2
0.06816311


CCC1(CCCC1═O)CC
0.0729801


CCCCCC(CC)C(═O)C1CC1
0.07530886


CC(C)C12CCC(C1)(CC2═O)C
0.07590504


CC(═O)C1CCC2C1CCCC2
0.08637492


CC1CCC2(CC1)C═CC(C2═O)C
0.08638542


CC1(C2CC(═O)C1(CC2═O)C)C
0.08683412


CCN1C2CCC1CC(═O)C2
0.0869081


CC1C═CC2(C1═O)CCCCC2
0.08745782


C1CCC(═O)NCCCC(═O)C1
0.0884375


CCC1CCCC(═O)CCC1CC
0.09197357


CC(C)OC1═NC═CC═CN1
0.09294477


C1CC2CC(C1)CC(═O)C2
0.09388228


C1CC2COCC(C1)C2═O
0.1089249


CC1(C(═O)CC23C1(CCC2)CCC3)C
0.1093057


C1CCC(═O)C2CCCC(C1)C2
0.1097336


CCCCCC1(CCCC1═O)CC═C
0.1102119


CN(C)C(═NS(═O)O)N(C)C
0.1104801


CC(C)(C═C)C1CCCC1═O
0.1145671


C1═CC═C(OC═C1)NCO
0.116571


C1C2CC3CC1C(C3═O)C═C2
0.1167317


COC1CCC(═O)C12CCCC2
0.1170204


CC1C2CCCN1CC2═O
0.1171827







Mor139-1








C1CCC2═C(C1)CCCC2═O
0.04565114


CCC(C)C1CCC(═O)CC1
0.04807124


C1CC2CC═CCC2C(═O)C1
0.04894259


CC(C)C1═CC(═O)CCC1
0.04953565


CCCC(C)C1═CCCC1═O
0.05030901


CC(C)CCC1═CCCC1═O
0.05046165


CC1CCCC2═C1CCC2═O
0.05373275


CC(C)CC1═CC(═O)CC1
0.05429959


CN(C)CCC1═CC═NC═C1
0.0645497


CC1═C2CSCC2CC1═O
0.0662884


C1CCC2(CCC2)C(═O)C1
0.06641502


C1C(═O)COC2═CC═CC═C21
0.06771594


CC1CCCC2═C1C(═O)CC2
0.06775099


CCCCC1CCC(═O)C1═C
0.07151075


CC1(C2C1C(═O)C(═C)CC2)C
0.07206624


CC1═CCCC(═C(C)C)C1═O
0.07226345


CC1CC(═O)C2═C1CCCC2
0.07233297


C1CCC(═C2CCCC2═O)C1
0.0727393


C1CC2CCC(═O)C═C2C1
0.07644502


CC(═CC1═CC(═O)CCC1)C
0.07737008


CC1CC2C(C2(C)C)CC1═O
0.07785543


CCCCCC1═CCCC1═O
0.07816834


CC1═CC(═O)C(CC1)C(═C)C
0.07862932


CCC(C)CC1═CCCC1═O
0.0793086


C1CCC2(CC1)CCC2═O
0.08011041







Mor162-1








CC1NC(═O)C2═CC═CC═C2O1
0.03923089


C1═CC═C2C(═C1)C═C(NO2)C═O
0.05289857


CC1═CN═C(C(═N1)C)C═O
0.06707111


C1C═C(C2═C(O1)N═CC═C2)O
0.06713544


C1═CC═C2C(═C1)C═C(C(═O)O2)N
0.06748666


C1CC2═CC3═C(C(═O)N2C1)NC═C3
0.06865916


CC1═CC(═C(C═C1)C═O)C
0.07170324


C1═CC(═CC(═C1)NC(═O)O)C═O
0.07340692


C1═CC(═C(C═C1C(═O)CO)O)O
0.07503177


CC1═CC(═C(C(═C1)O)C═O)O
0.07530568


C1COC(═N1)C2═CN═CC═C2
0.07562215


CC(═O)C1═CC2═CC═CC═C2C1
0.07665453


CC1═C(NC═C1C(═O)C)C
0.07707266


C1═C(NC(═C1)C2═CN═CO2)C═O
0.0776176


C1═CC═C2C(═C1)C(═O)C(═CO2)N
0.07804614


C1═CC2═C(NC(═O)C═C2)N═C1
0.07999517


C1═CC2═C(C═CC(═C2O)C═O)C═C1O
0.08200772


C1═CC═C2C(═C1)C═C(C═N2)O
0.08304375


CC1═C(NC2═C1C═C(C═C2)C═C)C
0.08360804


C1═CC(═CC═C1C═O)C2═NN═CO2
0.08368816


CC(═O)C1═CC═C(C═C1)N
0.08371712


CC(═O)C1═CN═CC═C1
0.08373728


C1═CC═C2C═C(C═CC2═C1)C═NO
0.0837472


COC(═O)C1═CC═C(C═C1)O
0.08383989


C1C═CC2═CC═CC═C2SS1
0.08392745







Mor170-1








C1═CC═C2C(═C1)C═C(NO2)C═O
0.06619393


CC1═NC(═O)C2═CC═CC═C2N1
0.0745505


CN1C═NC2═CC═CC═C2C1═O
0.08105557


CC1NC(═O)C2═CC═CC═C2O1
0.08323528


COC(═N)C1═CC═CC═C1
0.09673947


C1═CC═C2C(═C1)C(═O)N═CC═N2
0.1024509


C1C2═CC═CC═C2ON═C1C═O
0.1030488


C1═CC═C(C═C1)C(═O)CCN
0.1099358


CNCC(═O)C1═CC═CC═C1
0.1100806


CC1COC2═CC═CC═C2C1═O
0.1146739


C═CN1C═NC2═CC═CC═C2C1═O
0.1150157


C1═CNN(N═C1)C2═CC═C(C═C2)C═O
0.1202606


CC1═NC2═CC═CC═C2C(═O)N1C
0.1237537


C═NNC(═O)C1═CC═CC═C1
0.1254566


CN1NC(═O)C2═CC═CC═C2O1
0.1294035


CC1═NC2═CC═CC═C2C(═O)N1N
0.1318522


CNNC(═O)C1═CC═CC═C1
0.1335452


C1NC(═O)C2═CC═CC═C2S1
0.1342063


CC1═CC(═O)C2═CC═CC═C2O1
0.134347


CN1C(═O)C2═CC═CC═C2N═N1
0.1366252


C1═CC═C2C(═C1)C═C(C(═O)O2)C#N
0.1383896


C1═CC═C2C(═C1)C(═O)C═NS2
0.1389853


CC(═O)NN═CC1═CC═CC═C1
0.1393494


C1═CC═C2C(═C1)C═CC(═O)N2
0.1397765


CN1C(═O)C2═CC═CC═C2N═C1N
0.1422149







Mor184-1








CC1CCC(C(═C1)O)C(═C)C
0.2379638


CC(═C)C1CC═C(C(═O)C1)O
0.3728224


CC1═CCC(CC1═O)C(═C)C═O
0.3772072


CC1(CCCCC1═O)CC═C
0.3977689


CC1CC(═O)C(C(═O)C1)CC═C
0.4097808


CC1═CC(═O)C(CC1)C(═C)C
0.4227823


CC(═C)CC1(CCCCC1)O
0.4271719


C═CCC1CCC(═O)NC1═O
0.4313385


CC(═C)C1CCC(CC1)C═O
0.4430801


CC1CCC(C(═O)C1)C(═C)C
0.4515747


CC1C(CCC1C(═O)O)C═C
0.4557655


CC1═CCC(CC1O)C(═C)C
0.4625187


CC(═C)C1CCC(═CC1)C(O)O
0.4628184


CC(═C)C1CCC(═CC1)C(═O)N
0.472155


C═CCCC1C(═O)CCCC1═O
0.4769287


CC(CC═C)C1CCCCC1═O
0.4788082


CC1CCCC1(CC═C)CC═O
0.48704


C═CCCC(═O)C1(CCCCC1)O
0.4917173


C═CCCC1(CCCCC1)C═O
0.4941705


CC(═C)CC1═CCCCC1═O
0.4981998


C═CC1CCC(CC1)C(═O)OO
0.5022362


CC(═O)C(═C)C1CCCCC1
0.5079129


CC(═C)CC1(CCCCC1═O)C
0.5103795


CCCCC(═C)C1(CCCCC1)O
0.5108372


CC1CCC(C(C1)O)C(═C)C
0.5122089







Mor185-1








C1CC2═COC═C2CC1═O
0.02606647


C1═CC2═C(C═CC(═O)N2)N═C1
0.03118136


C1CC(═O)C2═C1C═CC═N2
0.0323404


C1CCC2═C(C1)CCC(═O)O2
0.03555811


CCC1CCCC(C1)N
0.0418893


CC(═C)C1═COC═C1
0.04258475


CCC1═CCC(CC1)O
0.04279004


CCC1CCC(CC1)N
0.04383469


C1CC(═O)C2(C1)CC2
0.04396265


C1CC(═O)C2═C1N═CC═C2
0.04402903


C1CN2CC(═O)OCC2CN1
0.04424295


C1C(CC2C1CNC2)N
0.046368


C1CCC2C(C1)CC2═O
0.04657459


C1CN2CCOC(═O)C2CN1
0.04905777


C1C2C═CC═C2C(CN1)O
0.04945669


C1CCC2(CC1)CC2O
0.04991706


C1CCC2═C(C1)C(═O)CN2
0.05012532


CCC1═C(CCCC1)O
0.0513685


CC(═O)C1═CCCC═C1
0.05278158


C1CC(═O)C2═CN═CN═C21
0.05282474


C1═CC2═C(NC═CC2═O)N═C1
0.05299349


CONCCN1CCCC1
0.05360856


CCC1CCCCC(C1)O
0.0536915


CC1═CC(═CCC1)OC
0.05373938


CCC1═CC═CC═C1N
0.05481358







Mor189-1








CC1(C2CCC1(CC2═O)C)C
0.04668916


CC(═C)C1CC═C(C(═O)C1)O
0.06676451


CC1═CC(═O)C(CC1)C(═C)C
0.1183577


C═C(C)C(CCC1C)═CC1═O
0.1347063


CC1CCC(═CC1═O)C(C)C
0.1680434


CC1═CCCC(C1═O)(C)C#C
0.1763185


CC1═CC(═O)CC(C1)C(═C)C
0.1962587


CC1═CC(CCC1═O)C(C)C
0.1976955


CC1C2(CCC1(C(═O)C2)C)C
0.2061102


CC1CC═C(C(═O)C1)C(C)C
0.2072536


O═C1C2C(CCC(C2)C1C)═C
0.2078362


CC1(CC(C(═O)C1)CC═C)C
0.2092703


CC(C)(C12CCC(═O)C1C2)O
0.2097237


CCC1═C(C(═O)C(CC1)C)C
0.2140007


CC1═CCCC(C1═O)(C)C═C
0.2180427


CCCC1(CCC(═O)C═C1)C
0.2194855


CC(C1C═CCCN1)C(═O)C
0.2209319


CC1═CCC(CC1)C(═C)C═O
0.2250367


CC1(CCC(═O)C═C1C═C)C
0.2310372


C1CN(CCC1CCN)C═O
0.2406093


O═C1C═C(C)CCC1C(C)C
0.241701


C═C1C═CCCC1CCC═O
0.2420684


CCC1═CC(═O)CCC1(C)C
0.2445011


CC1C(═C)C2CCC(C2)C1═O
0.2468526


CCCC1CCC(═O)C(═C1)C
0.2519408







Mor2-1








CC1C(═O)N(C1═O)C2═CC═CC═C2
0.1582261


CCCOC(═O)CC1═CC═CC═C1
0.1927919


C#CCOC(═O)CC1═CC═CC═C1
0.2135794


CC1CCC2═C(C1)SC(═N2)CC#N
0.2261997


C1CC1(C2═CC═CC═C2)OCCS
0.2307089


C═CCOC(═O)CC1═CC═C(C═C1)O
0.2685227


COC(═O)Cc1ccc(cc1)OC
0.2720388


CCC(C1═C(C═NC═C1)CO)OC
0.2840643


COC(═O)CC1═CC═CC(═C1)C#N
0.2858069


C1CCS(═O)(═O)C2═CC═CC═C2C1
0.2878607


C1CC1(C2═CC═CC═C2CO)O
0.2910175


CC(C)OC(═O)CC1═CC═CC═C1
0.2962586


C1═C(C(═C(N1)CN)CC#N)CCC#N
0.2973288


CC(CC1═CC═CC═C1)N═C═S
0.2995831


CCC(COCC1═CC═CC═C1)S
0.2998371


C1CC2═CC═CC═C2NC(═O)C1
0.3024153


C1CC(C2═C(C1)C═CS2)NC(═O)N
0.3030766


CC1(CN(C2═C1SC═C2)C═O)C
0.3041082


CCC(C1═CC═CC═C1N)C(═O)O
0.3045982


C1CC(C2═CC═CC═C2SC1)O
0.3073687


C1CC(═O)CCC2═C(C1)NC═C2
0.3082988


C1CCC(C(═O)CC1)C2═NC═CN2
0.3085543


CC(C1═CC═CC═C1N)C(═O)OC
0.3109102


C1═CC(═CC(═C1)CN)CC(═O)O
0.3115366


CCOC1═CC═C(C═C1)CC(═O)O
0.3116775







Mor203-1








CCCCC(═O)CCC
0.09870324


CCC(═O)CCCC(C)C
0.1234426


CCC1CCC(CC1)C(═O)CC
0.1310437


CC(CCCCCO)C
0.1429018


CCC1CC(C1)C(═O)C
0.1522912


CCC(═O)C═CCCC
0.1569658


CCCCCC(═O)CCC
0.1636356


CC═CC═CCCO
0.1710732


CC(═O)CC1CCCC═C1
0.1711094


C═CC1═CC═C(C═C1)CCCO
0.1778983


CCCCCCC(CCC)O
0.1785371


C#CC1═CCC(CC1)CCO
0.1786504


CC(CCCC═C)O
0.1865765


CC(═C)CCCC(═O)C
0.1901121


CCCCCC1CC(═O)C1
0.1926162


CCC(CCC═C═C)O
0.1948856


CC(C)CCCC(═O)C═C
0.1949919


CCCC═C═CCO
0.1959073


CCCCC(═O)NC
0.196173


C1CC(═CC═C1)CCCO
0.1965939


C(C═CC═CCC═CCC)O
0.1966562


CC(C)CCNC(═O)C
0.1977104


CC(C)C(CCCC═C)O
0.1995084


C1CC1═CCCCCO
0.2000587


CC(C)CC═CC(═O)C
0.2005665







Mor204-6








CC1(CCCC═C1C(═O)O)C
0.1592494


CC1CCC(═CC1═O)C(C)C
0.1767846


CC1CCC(═CC1═O)C(C)C
0.1767846


C1═CC═C2C(═C1)C═COC2═O
0.1926777


C1═CC(═O)NC2═C1C═NC═C2
0.2034395


CC1═CC(═O)C(CC1)CCO
0.2121746


C═CC1═CC═C(C═C1)C(═O)O
0.2283565


COC(═O)C1═CCCC(C1)O
0.2532418


C1═CC2═C(C(═C1)O)C(═O)NC═C2
0.2600318


C1═CC2═C(NC(═O)C═C2)N═C1
0.2607831


C1═CC(═O)NC2═NC═NC═C21
0.2758744


CN1C2C1C(CC(═C2)C(═O)OC)O
0.2813197


C1═CC2═C3C(═C1)NC═C3C(═O)C═C2
0.2828428


C═CC1═CN═C(C═C1)C(═O)O
0.2854501


CC(═C)CC1═CCCCC1═O
0.31266


CC1═CCC(CC1═NO)C(═C)C
0.3135219


C1C(═CC1═O)C2═CC═CC═C2
0.3180221


COC(═O)C1═CCC(CC1)SC
0.3191871


C1CC(═O)C2CC1C(═O)C═C2
0.3195927


CCC(═O)C1═CCCC(S1)C
0.3235687


C1CCC2(C1)CCC═C(C2═O)O
0.3305075


C1C(═CC(═O)N1)C2═CC═CC═C2
0.3309083


C1═CC═C2C(═C1)C═CC(═O)N2
0.3342322


CC1CC═C(C(O1)C)C(═O)O
0.3351069


CC1CCCC═C1C(═O)O
0.337837







Mor207-1








C1═CC═C2C(═C1)C═C(NO2)C═O
0.06933906


CC1═NC(═O)C2═CC═CC═C2N1
0.08035855


CC1NC(═O)C2═CC═CC═C2O1
0.08366296


CN1C═NC2═CC═CC═C2C1═O
0.09183173


COC(═N)C1═CC═CC═C1
0.1088847


CNCC(═O)C1═CC═CC═C1
0.1134785


CC1COC2═CC═CC═C2C1═O
0.1147292


C1═CC═C(C═C1)C(═O)CCN
0.1167073


C1C2═CC═CC═C2ON═C1C═O
0.1201505


C═CN1C═NC2═CC═CC═C2C1═O
0.124919


CC1═NC2═CC═CC═C2C(═O)N1C
0.1258814


C1═CC═C2C(═C1)C(═O)N═CC═N2
0.1260327


CN1NC(═O)C2═CC═CC═C2O1
0.1322961


CNNC(═O)C1═CC═CC═C1
0.1338931


CC1═CC(═O)C2═CC═CC═C2O1
0.1355292


CC1═NC2═CC═CC═C2C(═O)N1N
0.1374166


C1═CC═C2C(═C1)C═CC(═O)N2
0.1397979


CC(═O)NN═CC1═CC═CC═C1
0.1408284


C1═CC═C2C(═C1)C═C(C(═O)O2)C#N
0.1409447


CCOC(═N)C1═CC═CC═C1
0.1460253


CN1C(═O)C2═CC═CC═C2N═N1
0.1465203


C═NNC(═O)C1═CC═CC═C1
0.1469559


C1NC(═O)C2═CC═CC═C2S1
0.1485208


C1═CC═C2C(═C1)C(═O)SN2
0.1501398


C1C(C(═O)C2═CC═CC═C2O1)N
0.1512307







Mor223-1








C#CCOC(═O)CC1═CC═CC═C1
0.09948513


CCOC(═O)CC1═CC═CC═C1
0.1235873


CCCOC(═O)CC1═CC═CC═C1
0.1371426


C═CC(═O)OCC1═CC═CC═C1
0.160449


COC(═O)CC1═CC═CC═C1
0.2023979


CCC(═O)OCC1═CC═CC═C1
0.2040253


C1═CC═C(C═C1)CCCOC═O
0.2240541


C═C═CC(═O)OCC1═CC═CC═C1
0.228408


CCC(═O)OC1═CC═CC═C1
0.2532228


CC═C═CC(═O)OCC1═CC═CC═C1
0.2709531


C═CC(═O)OC1═CC═CC═C1
0.2744787


CC(═O)OCCCC1═CC═CC═C1
0.2756416


C═CCC(═O)OCC1═CC═CC═C1
0.2810861


C#CC(═O)OCC1═CC═CC═C1
0.2843047


C1═CC═C(C═C1)CCCCOC═O
0.2902787


CCCC(═O)OC1═CC═CC═C1
0.299426


CC#CCOC(═O)CC1═CC═CC═C1
0.3071297


CCCCC(═O)OC1═CC═CC═C1
0.3087009


COC(═O)CCC1═CC═CC═C1
0.3129654


COC(═O)CCCCC1═CC═CC═C1
0.3463383


CCCCOC(═O)CC1═CC═CC═C1
0.3527429


CC(═O)OC═CC1═CC═CC═C1
0.3589161


COC(═O)CCCC1═CC═CC═C1
0.3614484


C1═CC═C(C═C1)C═CCOC═O
0.3668489


C═COC(═O)CCC1═CC═CC═C1
0.3705043







Mor250-1








CCC(═O)OC1═CC═CC═C1
0.3793798


CCC(═O)OC1═CC═CC═C1
0.3793798


C1═CC═C2C(═C1)C═CC(═O)N2
0.384888


C1═CC═C(C═C1)OC(═O)N
0.4700359


CCC(═O)C1═CC═CC═C1
0.4719471


C1C(═O)C═CC2═CC═CC═C21
0.4828623


CC(═O)NC1═CC═CC═C1
0.4926594


C1C═CC2═CC═CC(═O)C2═C1
0.5147033


C1═CC═C(C═C1)NOC═O
0.534839


C1═CC2═CC═CC(═O)N2C═C1
0.5353036


C1C═C2C═CC═CC2OC1═O
0.5594665


C═C1C(═O)OC2═CC═CC═C2N1
0.572254


C1═CC═C(C═C1)ON═C═O
0.5755313


CC(═O)C═C1C═CCC═C1
0.5820786


C1═CC═C(C═C1)N(C═O)N
0.5914498


C1═CC═C(C═C1)NC(C═O)O
0.5925996


CC1═CC═C(C═C1)C(C)O
0.5991708


C1═CC═C(C═C1)ONC═O
0.6023895


C1═CC═C(C═C1)OC(═O)NS
0.6065903


c1(ccccc1)NC═O
0.6157112


C1═CC═C(C═C1)NC(═O)N
0.620347


CCC(C1═CC═C(C═C1)N)O
0.6529591


CC1═CC═CC═C1C(C)O
0.659726


C1═CC═C(C═C1)OC#N
0.668126


CC(C1═CC═C(C═C1)N)O
0.670643







Mor256-17








CCCCCCCCNC(═O)C
0.1277119


CCCCCCC(═O)OCCCC
0.1383745


C(CCCCN)CCCC(═O)O
0.1551848


CC1(CCCSC1)O
0.1810965


CCCCCCCCNC(═O)N
0.201845


CCCCCCOOC(═O)C
0.2354623


CCCCCCCC(═O)OCCC
0.2373345


CCCCCC(═O)OOCCCC
0.2398072


CCCCCCCCCC(═O)N
0.2537211


C(CCCC(═O)O)CCCN
0.256625


C(C)OC(═O)CCCCCCCC
0.2626695


C(CCCCC)OC(═O)CCCC
0.2654262


C1═CC═C(C═C1)CCCCCCN
0.2666345


CC(═O)NCCCCCCN
0.2891708


CCCCCCCOC(═O)CCC
0.2895618


CC1(NCCCN1)S
0.2910104


CCCCCCCCOC(═O)N
0.2941937


CCCCCCCCOC(═C)C
0.2947368


C1═CC═C2C(═C1)NC(═O)O2
0.2981597


CCCCCCNCC(═O)O
0.2988469


C1CCC(CC1)(O)S
0.3023335


C1═CC═C(C═C1)CCCCCNO
0.3041483


CC(═O)CCCCOC(═O)C
0.3058697


C1═CC═C2C(═C1)NC(═O)N2
0.3085566


C1═CC2═CNON2C═C1
0.3135061







Mor258-1








C1═CC═C2C(═C1)NC(═O)C═N2
0.04626341


CC1═NC2═CN═CN2C═C1
0.06964932


C1═CC2═C(N═CN2C═C1)C═O
0.07254368


CC1═CC2═C(C═C1)C═C(S2)N
0.07338813


C1CC(═CC═C1)C═C═O
0.08079227


CC1CC═NC═N1
0.08086793


C1═CSC(═C1)C2═NN(C═C2)O
0.08123408


C1CNC(C═C1)C═O
0.08133689


CC1═NC2═C(C═C1)NC═N2
0.08243389


CNC1═CC═NC═C1
0.08274814


CC1═NC(═NC═C1C#N)C
0.08415395


CC1═CCC(═O)CC1
0.08472221


C1═COC(═C1)C2═NN═NC═C2
0.08486507


CC1═CC2═NC═NN2C═C1
0.08532597


CC(═O)OCSC
0.08674062


C1COC2═C1C═C(C═C2)C(═O)O
0.08754144


C1═CC═C2C(═C1)C═CN2N═O
0.08813593


C1═C2N═C(N═CN2N═C1)C#N
0.08876163


C1═CC2═C(N═C1)SC(═C2)C═O
0.088843


C1CC2═C(C═C1)NNC(═O)C2
0.08899985


C1CSN(N1)COC(═O)N
0.0890455


C1CC(SC1)CO
0.09011572


CC1═NN(CS1)C
0.09045361


C1CC2═C(C1)SC(═N2)N
0.09100104


C1C═CC2═CN═NC2═C1
0.09216601







Mor259-1








C1═CC═C2C(═C1)NC(═O)C═N2
0.04197753


C1═CC═C2C(═C1)NC(═N2)C═O
0.09240541


C1═CC2═C(NN═C2C═C1)C═O
0.0990186


C1═CC═C2C(═C1)C(═CC2═O)O
0.10483


CC(═O)NC1═CC═CC(═C1)C(═N)N
0.1129747


C1═CC2═COC(═C2C═C1)C═O
0.1146544


C1═CC═C2C(═C1)NC(═O)O2
0.11715


C1C2═CC═CC═C2ON1C═O
0.1209336


C1═CC═C2C(═C1)NC(═O)NN2
0.1401864


C1═COC(═C1)C2═CC═C(C═C2)O
0.1428006


C═C1C2═CC═CC═C2OC1═O
0.149781


C1═CC(═CC═C1C(═O)O)NCCC#N
0.1550444


CN═CC1═CC═C(C═C1)C(═O)OC
0.1553915


C1═CC═C2C(═C1)C═COC2═O
0.1566442


C═CC(═O)NC1═CC═C(C═C1)C(═N)N
0.1586167


C1═CC═C2C(═C1)C═NN2C═O
0.1601506


CN═NNC1═CC═C(C═C1)C(═O)OC
0.1621242


C1═CC═C2C(═C1)C(═CO2)N═O
0.1633878


COC1═CC═C(C═C1)NC(═O)C═C
0.1651134


CC(═O)NC1═CC═C(C═C1)N(C)C
0.1651899


CCC(═O)C1═CC═C(C═C1)OC
0.1667638


C1═CC═C2C(═C1)C═C(C═N2)O
0.1670003


CCNC1═CC═C(C═C1)N(O)O
0.1673194


CCC(═O)C1═CC═C(C═C1)NO
0.1704537


C1═CC═C2C(═C1)OS2(═O)═O
0.1718171







Mor260-1








COC(═O)CCNC1═CC═CC═C1
0.04581804


C═CC(═O)CCCCCCCC(═O)O
0.04753727


C#CCCCCCC═CC(═O)O
0.04908247


CCOC1═CC═C(C═C1)OC(═O)NC
0.05223202


CCCCC(CC)COCC(═O)O
0.06334015


C1CCC(C1)C(═O)CCCCC(═O)O
0.06629472


CCCCNC1═CC═C(C═C1)OC
0.06633087


CCCCNC(C)CC(═O)OC
0.0667401


CNC(═O)COC1═CC═C(C═C1)C#N
0.07077628


CCCCCCCCC(═O)C(═O)O
0.07088963


CCCCCCCCCOC(═O)O
0.07169935


CCCCCC1CCC(CC1)C(═O)O
0.07283703


CCNCCC1═CC2═C(C═C1)OCO2
0.07466881


CC(═O)CCC(═O)NC1═CCCCC1
0.07735112


CCCC1CCC(CC1)CCC(═O)O
0.07825345


COC(═O)C═CCNC1CCCCC1
0.0817986


CCOC(═O)CCNC(═O)CC═C
0.08188413


CNCC1═CC═C(C═C1)N2CCCC2
0.0845592


CCCCCCOC(C)CS
0.08794286


CCCCCC(═O)OC(C)S
0.08888077


CCCCCCOC(═O)CC(═O)O
0.08920102


CCNCC1═CC═C(C═C1)C(═O)OC
0.08954888


CCCCOC1═CN═C(C═C1)C(═O)O
0.08968592


COC(═O)CNCCC1═CC═CC═C1
0.09056786


CCCCCCCC(═O)NCC
0.09156643







Mor261-1








CCCCCC(CCO)O
0.1725635


CC(═O)SCC1═CC═C(C═C1)C═C
0.2079287


CCCCC(CCCCO)O
0.2205779


CCNC1═CN(N═C1)C2═CC═CC═C2
0.2643034


CCCCCCCCNN
0.2799805


CC1═CC═C(C═C1)NCC(═O)NN
0.2822771


CC1═CC═C(C═C1)N2C═C(N═C2)CO
0.2832535


CCCOC(═O)C1═C(C═C(C═C1)N)O
0.2852718


CCCCN1N═C2C═CC═CC2═N1
0.2984866


CCCCNC(═O)OC1═CC═CC═C1
0.3016491


CCCC(═O)CCCCCO
0.314819


CCCCCOC1═CCCC═C1
0.323052


CCCCSC1═CC═C(C═C1)N
0.3336667


CCOC(═O)CC1═CC(═C(C═C1)N)O
0.3378758


CC1═CN═C(C═C1)NCCC(═O)O
0.3419532


CCOC1═CC═C(C═C1)NC(═O)NN
0.349067


CCCCC═CC═CCO
0.3594656


CCCCCCC(CCO)O
0.3622535


CCOC(═N)CCCCC#N
0.3642063


CCCOC(═O)SC1═CC═CCC1
0.3687816


CCCOC1═CC═C(C═C1)CC(═N)N
0.3695899


CCCCCSC1CCCC1
0.3784109


CCOC1═CC(═NO1)C2═CC═CC═C2
0.3805914


CC1═CC═C(S1)CNC2CCCC2
0.3893983


CC1═CN(N═C1)CCCC(═O)O
0.3914392







Mor268-1








C═C(CCO)CC1═CC═CC═C1
0.08680266


C1═CC═C(C(═C1)CCCCO)S
0.09776434


C1═CC═C(C═C1)CC2═C(OC═C2)CO
0.1173957


CC1═CC═CC═C1C2═CC(═NO2)CO
0.1177446


C═CCC1═CC═CC═C1C#CCO
0.12333


CCCC1═CC(═C(C═C1)CN)N
0.124973


CC1═CC═CC═C1CCCCO
0.1258311


C1═CC═C(C(═C1)CCCCN)N
0.132615


CCCCC1═CC═CC═C1CCN
0.1390437


C1═CC═C(C═C1)N2C═C(C(═N2)N)CO
0.1412993


CCCCCCN(C)C(═S)N
0.1478017


C1CCCCCCCCC
0.1517669


C═C═CCC1═CC═CC═C1CCO
0.1577928


CN1C(═CC(═N1)C2═CC═CC═C2)CO
0.1577991


C1═CC═C(C═C1)NNC(═O)CN
0.1627573


C1═CC═C(C(═C1)CCCCO)N
0.1671111


CC1N═C(NN1C2═CC═CC═C2)CO
0.1706577


CCCCCCC1(CNC1═O)C
0.1736143


C1CCC(═CCCCO)CC1
0.1749776


CC(═CCCC(═CCN)C)C
0.1790916


C1═CC═C(C(═C1)C#CCCO)N
0.1844991


CC(═C)CCCCCCO
0.1861627


C1═CC═C(C(═C1)CCCCN)S
0.192109


C(CCCCCC═C)O
0.1949365


CC1═C(C═C(C═C1)CCCN)C
0.1985303







Mor271-1








CC(═O)CCC(═O)C
0.00696499


CC(═C(C)N═NC)C
0.02227481


CC═C(C)C(═O)OC
0.02303129


CCC(═C)C(═O)OC
0.03826216


CN(C)C(═O)CC═C
0.03853879


COC(═C)C(═C)OC
0.05227276


CC(C═C)C(═O)OC
0.05905649


C1CCC(CC1)C2C═CCOO2
0.05928864


CCCC1SCCS1
0.059703


CCOCC(═O)C(═C)C
0.06042718


COC(═C(C#N)C#N)C1═CC═CO1
0.06070587


CCCC1(C(O1)(C)C)C(═O)OCC
0.06170662


CCN1C(═C(C(═C(C1═O)C)C)C)C
0.06559493


C═CC(═O)OC1CCCCCC1
0.06979654


CCN═C1N2CCCC2CCS1
0.07527722


CC(C)CC(═O)SC
0.07652444


CC(C(═O)C═C)OC
0.07826366


CSC1CCCCO1
0.0787251


CC#CC(═O)N(C)C
0.0789155


C═CCC(CC1CCCCC1)C#N
0.07892005


COC(═O)C(═C1CCCCC1)C#N
0.079376


C═C═CCN1C(COC1═O)C#C
0.08053451


CCC(═O)C1C(═O)CC(CC1═O)(C)C
0.08254048


CC1C═CN(C═CC1═C)C(═O)C
0.08378967


CC1═CC═CC(═C1C#N)C(═O)OC
0.08382356







Mor272-1








CC1CCC(═O)C1CCC#C
0.03189391


CC1═C(C/C═C\C)C(CC1)═O
0.0385644


CC1COC(O1)C(C)(C)C
0.03930837


CC(C)(C)C1OCCCO1
0.04139548


CCCCCC(═O)C═C
0.04630508


CCC(═C)COC(═O)C
0.04645563


CC1═NC(OC1)C(C)C
0.04785742


CCCC(═O)CCC
0.04868388


CC(═O)CCCCCC
0.05098426


CCCCCCC(═O)C
0.05098426


CCC1═NC(CO1)(C)C
0.05112795


CC1(C(═O)C(S1)(C)C)C
0.05313001


C(C)OC(═O)C(═C)C
0.05386116


CCCC1C═C(C(═O)O1)C
0.05455699


CC(C)(C)C1OCC(═C)O1
0.05557511


C═C1CC(CO1)C2═CC═CC═C2
0.05582976


CC(C)(C)C(═O)N1C═CC═C1
0.05717043


CC1═NCCN1CC2═CC═CC═C2
0.05790857


CC(C)C(═O)OC═C
0.05836228


CC(═C)C(═O)OC(═C)C
0.05855966


CC(═O)CCCCC═C
0.05864676


CC(═O)OC1CCCC═C1
0.05871441


CCOC(═O)C1(CC1)C
0.06032142


CC(═CC)COC(═O)C
0.06154408


CCC(C)COC(═O)C
0.06223368







Mor273-1








CC1(C2CCC(C2)(C1═O)N)C
0.05501643


CCC1(C2CCC(C2)C1═C)CC
0.0736611


CC1CC2═CC═CC═C2N1
0.07383829


CCCCC1═C(OC═C1C)C
0.07413368


CC1CNC2═CC═CC═C12
0.07595554


CC(C)C(═O)OC═C
0.0776473


CCC(C)NC
0.08056587


CCSNC(C)C
0.08292255


CCCCC1═C(CCCC1═O)O
0.08494866


CCCC(C)OC
0.08782356


CC12CCCC1═CC═CC2
0.08803756


CC1CC1C2═CC═CC═C2
0.08885662


CC1CCC(S1)C
0.08906697


CN(C)C(═O)COC
0.08911168


CC1C2═CCC(C2)C1(C)C
0.08918327


CC1CC═C(C1)C(═O)C(C)C
0.09082242


CC1CC2═C(C(═C(C12)C)C)C
0.09087068


CC1CC═CC12CCCC2═O
0.09092754


CC1CCCC═C1C(═O)N(C)C
0.09201093


CC1═C2C(═NC═NC2═NN1)N
0.09262628


CC1═CC(C(CC1)C(C)(C)O)O
0.09308545


CN1C═NC2═C1C(═NC═N2)N
0.0936681


CC(═NOC(═O)C(C)(C)C)C
0.09476478


CC(C)C1CCCCC1═O
0.09521714


CC1═C(C(═O)N(C1═O)NC)C
0.09626298







Mor277-1








CC1(C2CC(═O)C1(C═C2)C)C
0.1480439


CC1C2(CCC1(C(═O)C2)C)C
0.1501646


C1CC(CC(═O)C1)N
0.173522


C1C(C2═CC(═O)C(═O)CC2═N1)O
0.1843483


CC1CCCC(═O)C1
0.1871075


C1CC(═O)C2CC═CC1S2
0.2020606


CCC12CCC(C1(C)C)CC2═O
0.2177276


C1CNC2═C(C1═O)C═CC═N2
0.2429412


CC1COCC(C1═O)C(C)(C)C
0.2469828


C1CC(═O)C2═C(NC1)N═CC═C2
0.2562849


C1CC(═O)C2═CC═CC═C2OC1
0.2580454


CC(C)C12CCC(C1)(CC2═O)C
0.2605051


CC1(C2(CCC1(C(═O)C2)C)C)C
0.2605627


C1C2CC3C═CC2CC1C3═O
0.2618416


CCC1(C(═O)CC12CC2)CC
0.2665882


C1CC(CC(═O)C1)S
0.2680335


CC1CCCC12CCC2═O
0.268307


CC1CCCC(═O)C1(C)C(C)C
0.2696565


CC1CCCC(═O)C1(C)C
0.2719524


CC1(CC(═O)CC2(C1O2)C)C
0.2734493


C1C═CNCC1═O
0.2802712


CC(C)C1CC(═O)C═CN1
0.2822056


C1(═O)C═CCCC1
0.2823245


CC(C)(C)C1CC(═O)C═CN1
0.2876126


CC1CC(═O)C═CN1
0.2899973







Mor30-1








C(═O)(CCCCCCCCCC)O
0.3122994


C#CCCCCCCC(═O)O
0.3287775


C═CCCCCCCCCC(═O)O
0.3395462


CC#CCCCCCC(═O)O
0.3545879


C(═O)CCCCCCCCCC
0.3841893


C═CCCCCCCC(═O)O
0.4070303


C(═O)CCCCCCCCCCC
0.4113624


CCCC#CCCCC(═O)O
0.4176973


CCCCCC#CCC(═O)O
0.4183262


CC#CCCCCCCCCC═O
0.4361534


CCCCCCCCCCCCC═O
0.4486903


C#CC#CCCCCCC(═O)O
0.4608185


CC(C)CCCCCCC═O
0.4667896


CCC#CCCCCC(═O)O
0.4774187


CC(═C)CCCCCCC(═O)O
0.478635


CCCCCCC1CC(═O)O1
0.4816979


C═CCCCCCCC═O
0.4847203


C#CCCCCCCC═O
0.4893661


C═CCCCCCCCCCC═O
0.4941443


C1C═C1CCCCCCC(═O)O
0.4958194


C═CCCCCC1CC(═O)O1
0.4999771


C(═O)(CCCCCCCC═C)O
0.5103754


C1C═C1CCCCCCCC(═O)O
0.5167787


CC(C)CCCCCCCCCC═O
0.5233348


C(═O)CCC═CCCCC
0.5329278







Mor33-1








C1C═C1CCCCCCC(═O)O
0.03288254


C(═O)(CCC═CCCCCC)O
0.1022495


C(═O)(CC═CCCCCCC)O
0.1268822


CCCCCCC═CCCCC(═O)O
0.1272125


C(C)C(CCC(═O)O)CCCC
0.143634


CC(C)N1C═C(C═N1)CCC(═O)O
0.149314


C═C(CCCCC#N)C(═O)O
0.1541986


CC#CCCCCCCCC(═O)O
0.1626916


CC(CCC(═O)O)N═NC(C)(C)C#N
0.1652641


CC(C)CCCCCC(═O)O
0.181324


CCCC(═C)CCC(═O)O
0.1901408


CC(C)C1CCC(═CC1)CCC(═O)O
0.1912924


CCCCC═CCC(═O)O
0.1939872


CCCCC#CC#CCCCC(═O)O
0.2014499


C1C═CC═CC1CCC(═O)O
0.2309548


C1C═C1CCCCCCCC(═O)O
0.2314075


C(═O)(CCCC═CCCCCCCCC)O
0.2344658


CC#CCCCCC(═O)O
0.2381936


CCC1═CC(═C(C═C1)CCC(═O)O)C
0.247278


CCC1═CC═C(C═C1)CCC(═O)O
0.253656


CN(C)C1═CC═C(C═C1)CCC(═O)O
0.2546323


CC1(CCC(═CC1)CCC(═O)O)C
0.255894


CCC(C)OC(═O)CC(═O)O
0.2640391


CCCC(CC(C)C(═O)O)C#N
0.2650706


C#CCCCCCCC(═O)O
0.2760099







Mor37-1








CCCCCCCCCCCCCC(OC(C)C)═O
0


CCCCCCCCC═O
0


CCCCCCCCCC═O
0


O═C(C)CC/C═C(CC/C═C(C)/C)\C
0


C(═O)(CCCCCCCC)O
0


C(═O)(CCCCCCCCC)O
0


C(═O)(CCCCCCCCCCC)O
0


C(═O)(CCCCCCCCCCCC)O
0


C(═O)(CCCCCCCCCCCCC)O
0


C(═O)(CCCCCCCCCCCCCCC)O
0


CC1═C(C═C(C═C1)[C@H]C)CCC═C(C)C)O
0


CC(OCCCC/C═C\CCCC)═O
0


CC/C═C/CCCCCCCCCC([H])═O
0


CCCCCCCCCC[C@H](OC(C)═O)[C@@](O1)([H])CCCC1═O
0


O═C(CCCCCCC)N1CCC(C2═CC═CC═C2)CC1
0


O═C(CCCCCCCC)N1C(CC)CCCC1
0


O═C(CCCCCCCCC)N1C(C)CCCC1
0


O═C(N1CCC(C)CC1)CCCCCCCCC
0


O═C(CCCCCCCCC═C)N1CCCCC1
0


O═C(CCCCCCCCC═C)N1C(CC)CCCC1
0


O═C(CCCCCCCCC═C)N1CCC(C2═CC═CC═C2)CC1
0


O═C(CCCCCCCCC═C)N1CCC(C)CC1
0


O═C(CCCCCCCCCC)N1CCCCC1
0


O═C(CCCCCCCCCCC)N1C(C)CCCC1
0


O═C(CCCCCCCCCCC)N1CC(C)CCC1
0







Mor40-1








C1═CC═C(C═C1)CCC2═NN═C(C═C2)N
0.02341789


C(═O)(CCCCCCCC═C)O
0.03694565


COC1═CC═C(C═C1)CCCCC#N
0.04434623


C═CCCCCCCCC(═O)S
0.04698026


CNC1═CC═C(C═C1)CCC(═O)OC
0.04732122


C(CCCC(O)O)CCCC(═O)O
0.04909072


CCCCC(═O)C1═CC═C(C═C1)OC
0.05102408


CCCCCC(═O)C1═CC═C(S1)O
0.05385012


CCCCCCC(CC(═O)O)O
0.0581458


CCCCCCCC(═O)OS
0.0582002


CCCCC#CC(═O)CCCC
0.05901835


CC(═C)CCCCCCC(═O)O
0.06104174


CCCCCCCC(═O)C#N
0.06314685


CCCCCCCC(═O)C═C═C
0.0637365


COC(═O)CCCCCC═CC═C
0.06446715


CCCCCCC(═O)OC(C)S
0.06476652


COC(═O)C1═CC═C(C═C1)CCC═O
0.06633289


CCCCCCCC(═O)NO
0.06904818


COC(═O)CCC1═CC═C(C═C1)C═C
0.07307333


CN(C(═O)CCCC1═CC═CC═C1)O
0.0740539


CCCCCCCC(═O)N(C)N═O
0.0767582


CCCC(═O)C═C═CC1═CC═CC═C1
0.07690233


CCCC(CCCC(═O)C═C)O
0.07774642


COC(═O)CCCCCCC#C
0.07783589


COC(═O)CCC1═CC(═CC═C1)NN
0.07920649







Mor41-1








C1C(═O)C2═CC═CC═C2ON1
0.1690913


C1═CC═C2C(═C1)C═CC(═O)N2
0.19021


C1CC2═C(C═C1)OC(═O)C═C2
0.195202


CCCCC1═C(NNC1═O)C
0.1966665


CC═C1CCC(═O)CC1
0.2004243


C1C═CC2═C(C1═O)C═CC(═O)O2
0.2108212


CCCCC1═C(OCC1═O)O
0.2185848


C1NC(═O)C2═CC═CC═C2S1
0.2228905


C1C(═O)C2═CC═CC═C2OS1
0.2251387


C1═CC2═NNN═C2C═C1C(═O)N
0.2380402


CC1CC1(C2═CC═CC═C2)O
0.2385417


CC1CCC(═CC1═O)C(C)C
0.2447182


C1═CC═C(C═C1)S(═O)N
0.2462735


C1CNC2═CC═CC═C2C1═O
0.2512114


CC1(C(═O)O1)C2═CC═CC═C2
0.252103


C1═CC═C2C(═C1)C(═O)NNN2
0.255054


C1CSC2═CC═CC═C2C1═O
0.2633634


C1═CC2═NSN═C2C═C1C(═O)N
0.2647783


CS(═O)C1═CC═CC═C1
0.2651372


C1C(═O)C2═CC(═C(C═C2S1)O)O
0.2656982


C1C(C(═O)N1)C2═CC═CC═C2
0.2658363


C1═CC2═NON═C2C═C1C(═O)N
0.2685111


CC(═C1CCCCC1)O
0.2698948


C1═CC(═C(C═C1O)C(═O)NO)O
0.2742529


CN(C1═CC(═O)CCC1)O
0.2797649







Mor5-1








CCCCC(C)CC(C)C(═O)O
0.00047992


CCCN1CNC═C1CC(C(═O)O)N
0.00047992


CCCCC═CCC(═O)O
0.00047992


CC1═CC═C(C═C1)CC(C(═O)O)N
0.00047992


C1CCC2═C(C1)CCNC2CC(═O)O
0.00047992


CCC(C)(C)C(C)C(CC(═O)O)N
0.00095984


C1CCC(C1)C═C═CCCC(═O)O
0.00095984


CCSCCCC(═C)C(═O)O
0.00095984


C1CCC2═C(C1)C═NC2CC(═O)O
0.00095984


CCCCN═C(C)CC(═O)O
0.00095984


C(CC(C(═O)O)N)CNCC(═N)N
0.00143976


C1CC(CC═C1)CCC(═O)O
0.00143976


C1═CN(C═N1)CCCCCCC(═O)O
0.00143976


CC(═C═C1CCCCC1)C(═O)O
0.00143976


CCCCCCC═CC(═O)O
0.00143976


CC1═C(C═NN1C(C)C)CCC(═O)O
0.00143976


C1═CC═C(C(═C1)CCC(═O)O)CN
0.00143976


CCN1C(═C(C(═N1)C)CCC(═O)O)C
0.00191969


CCC(CCCCC(═O)O)S
0.00191969


CCC1═CN═C(C═C1)CCC(═O)O
0.00191969


C(CCCC(═O)O)CCCS
0.00191969


CCC1═CC═C(C═C1)N(CC(═O)O)N
0.00191969


CSCCCC(C(═O)O)N
0.00191969


C1═CC(═CN═C1)CC(CN)C(═O)O
0.00191969


CCCCC1NC(CS1)C(═O)O
0.00191969







Or1A1








CCCCC(═O)CCC
0.06049592


C═CCCC(═NO)C1═CC═CC═C1
0.07078234


CCCOCCC(═O)C═C
0.0717614


C1CCCN(CC1)C(═O)N2CC2
0.07369093


C1CCC2═C(C1)C═CC═C2CC(═O)N
0.07424287


CCC(═NO)C1═CC═C(C═C1)C
0.07771264


CC1CC═C(NC1═O)C(C)C
0.07826973


C1CC(═O)N(N═C1)CC2═CC═CC═C2
0.07849599


CC1(OCC(CO1)C(C═C)O)C
0.07977273


CCCC(CC1═CC═CC═C1)C(═O)N
0.08055253


CC1═C2C═(C═CC2═NC═C1)C(═O)N
0.08126738


C1CC(C2═CC═CC═C2C1)CC(═O)N
0.08161454


COCC1═CC═CC2═C1CCCC2═O
0.08240595


CC(CC═C)OC(═O)C1═CC═CC═C1
0.08262245


CC(═O)C1═CC2═C(C═C1)OCCNC2
0.08372653


C1C═CC═C(C1═C═O)CC2═CC═CC═C2
0.0853223


CCOC(═O)CC(C)C1═CC═NC═C1
0.08749861


CC1═NC2═CC═CC═C2C═C1C(═O)N
0.08760062


CCC(C(═O)C)NC1═CC═CC═C1
0.08768825


CCCNC(═O)C1CCCN1C
0.0881405


C1C(NC(═O)CO1)C2═CC═C(C═C2)N
0.0887641


CC1═C(N═NC2═CC═CC═C12)C(═O)C
0.08925951


CCCCC(C1═CC═CC═C1)(O)O
0.08964993


CC1(OCCO1)CC2═CC═CC═C2
0.0898149


CC1(CCCNN1)C2═CC═C(C═C2)N
0.09071153







Or2J2








CCCCC(CCCCO)O
0.1878163


CC(C)CCCCCCO
0.2001934


C(CCCCN)CCCCON
0.2449217


C═CCCCCCC1CO1
0.2829139


CCCCCCNCC(CC)O
0.2913393


CCCCCCCCN(C)O
0.3036347


CC(C)CC1COC(N1)CCO
0.3482781


CCCCCCCCONC
0.3506971


CCCCCCC(C)NCCO
0.35406


CCCCCN1CCC(C1)CO
0.3679643


CCCCCOC(C)CCO
0.3976565


CCCCCCCC(C)CO
0.4018389


CCCCCCCCNOC
0.4019279


C(CCCCNCCO)CCCN
0.4055974


CC(C)CCCCNCCO
0.4075006


CCCC1CC(C1O)O
0.4174384


CCCCC(C)(CC(C)O)O
0.4285046


CCCC1CCN(CC1)CCO
0.4289913


CCCCCCCCCCNO
0.4353896


C═CCCCC#CCO
0.4379203


CCCCCCNC(C)CO
0.4486621


CCC(C)CCCCCCO
0.4517683


CCCCCCC1CCNO1
0.4523566


CCCCC(CC(C)(C)O)O
0.459996


C1═CC═C(C═C1)C(═O)NCN═C═O
0.462984







Or2W1








CCCC(CCC═C(C)C)O
0.00049109


CC(C)CC(═O)CC(C)C═C
0.00069469


C1CCC(CC1)C═NC(═O)CO
0.00069469


C1C═CNNC2═CC═CC═C21
0.00069469


CCCOC1═CC═C(C═C1)C
0.00085074


CCCCCC1CCC═CO1
0.00085074


CC(C)NCCCNCCCN
0.00098218


CC(═CCCC(C═C)C═O)C
0.00098218


C═C═CCCCCO
0.00098218


CCCCCOCC#N
0.00118032


CC(C)CCCC(C)C1CO1
0.00120303


CC(═O)C1CCC(═C)C(═C)C1
0.00120303


CC(C)(C)C(═O)OCCCO
0.00120303


CC(C)C(═C)C(═O)NC1═CC═CC═C1
0.00127841


CC(C)NC(═O)CC(CCN)N
0.00129418


COCCOCCOCC1═CC1
0.00129719


CCCCC(═C)N
0.00138938


CCCCC═C═CC(C)(C)O
0.00138938


COC(═O)CC1═CC═C(C═C1)C═C
0.00143135


CC(C)NCC1═CC═C(C═C1)NC
0.00145282


CN(C)CC#CCCCC#C
0.00147327


C═CCCCCO
0.00147361


CC#CC(═O)C1CCCCC1
0.00147361


CCCN1C═C(C═N1)C(C)NC
0.00147361


CC1(CC1C(═O)NC2═CC═CC═C2)C
0.00153553







Or5P3








C═CC(═O)C1CCCCC1
0.3110759


CC1═CC(═O)C(CC1)C(═C)C
0.3586025


C1═CC═C2C(═C1)C═CC(═NO)O2
0.3721933


C1═CC═C2C(═C1)C═CNC2═S
0.3723315


C1═CC═C2C(═C1)C═COC2═O
0.3945556


C═C1C2═CC═CC═C2ONC1═O
0.3977094


C1═CC═C2C(═C1)C═CC(═O)N2
0.4009083


CC1═C(C(═O)CC1)CC═C
0.4052338


C1═CC(═CC2═C1C═CC(═O)O2)S
0.4053821


CC1═CCC(CC1═NO)C(═C)C
0.4125247


CC1(C2C1C(═O)C(═C)CC2)C
0.4507506


CC1C═CC(═O)C12CCCCC2
0.452584


CCC12CCC(═O)C═C1CC(C2)O
0.4690015


C1═CC═C2C(═C1)C═C(C(═O)O2)O
0.476153


C1CCC2(CC1)CCC═CC2═O
0.48111


C═C1CC2CCCCC2C1═O
0.4838933


C1═CC═C2C(═C1)C(═S)C═CN2O
0.4840491


C1CCC2(CC1)CC═CC(═O)C2
0.4924455


CC(═C)C(═O)CCC#C
0.4929412


C═C1CC2(C1═O)CCCCC2
0.5054476


CC1═CC2CCCC(═O)C2═CC1
0.5090888


C1CC═CC2(C1)CC═CC(═O)NC2
0.5100904


CC1═C2C(═C)C(═O)NC2═CC═C1
0.5151608


C1═CC2═C(C(═C1)O)OC(═O)C═C2
0.516556


CC(═O)C1═CCC2(C1)C(═C)CCC2═O
0.5272902


CCC1═CC2CCCCN2C1═O
0.5776696


C1═CC═C2C(═C1)NC(═O)C═CS2
0.5831019


CC1═C2C═CC(═O)NC2═CC═C1
0.5841847


C1CC(═O)C2═C(C═C1)C═CC(═C2)O
0.5848835


C1═CC2═CN═C(C(═O)N═C2C═C1)N
0.5849539


C1═CC═C2C(═C1)C═CN3C2═NNC3═O
0.5850339


C═C1CC2═CC═CC═C2OC1═O
0.5862692


CCC(═C)CC1═CCCC1═O
0.5878639


CC1═CC(═O)OC2═C1C(═C(C═C2)O)N
0.5881438


CC12CC═CCC1CC(═O)C═C2
0.5900818


C═CCC1CCC(═O)C═C1
0.5902211


C1CC2(CCC═CC2═O)CC═C1
0.5931621


CC1═CC(═O)NC2═C1C(═CC═C2)N
0.5952647


CC1═C(CCC1(C)C)C(═O)OC
0.5982892


CC1(CC1C(═O)C═C)C
0.5994571


C1C2═CC═CC═C2C═C(C1═O)O
0.6030376


CC1═CCC(CC1═O)C2(CO2)C
0.6032878


CC1C═C(C(═O)O1)C2═CC═CS2
0.6051044


CC1═CC(═O)CCCC1CC═C
0.6074293


CC(C)C1CCC═C1C(═O)C
0.6076714


CC1═CC(═O)OC1C2═CC═CC═C2
0.6080884


CC1═CC(═S)C2═CC═CC═C2O1
0.6085333


C1C2═CC═CC═C2C(═O)C1═CN
0.608787


C1═CC2═C(C═CC(═O)O2)C═C1N
0.6088277


C1CC2(CC3CC2C═C3)C═CC1═O
0.6095803









The approach described herein was also used to predict activators of neurons that are responsive to CO2. In order to train the platform to predict CO2 neuron activators a large panel of odors was assembled that have previously been tested against CO2 responsive neurons in several species. The panel comprises 108 odors, which have been tested against one or more of the following species: Anopheles Gambiae, Culex Pipiens, Aedes Aegypti, Drosophila Melanogaster. The panel consists of a broad collection of functional groups including alcohols, esters, acids, ketones, alkanes, aromatics, terpenes, and heterocycles. The activities of these odors were normalized from 100 to −100 representing the range from the strongest observed activator to the most inhibitory, respectively. Upon normalizing, it was observed that the strongest activators were heterocycles and some moderate activators were non-aromatic cyclic compounds. These distinct structural differences would likely drastically alter the outcome of the predictive platform. Due to this, the dataset was divided odors into two distinct sets. The first set focuses on activating odors with aromatic structures that look very structurally distinct from inhibitors. This set does not include non aromatic activators, activators which share structural characteristics with inhibitory odors, or odors which inhibit the receptor at greater than 30 percent of maximum. The second set is broader in scope and consists of odors both aromatic and non-aromatic structures as well as all inhibitory odors.














Final
Training
Training


Odor Name
Activity
Set 1
Set 2


















butanal
−85
No
Yes


pentanal
−51
No
Yes


hexanal
−32
No
Yes


heptanal
−21
Yes
Yes


octanal
−20
Yes
Yes


butanol
−25
Yes
Yes


pentanol
−41
No
Yes


hexanol
−70
No
Yes


heptanol
−38
No
Yes


octanol
−35
No
Yes


butanone
−25
Yes
Yes


pentanone
−28
Yes
Yes


hexanone
−19
Yes
Yes


heptanone
−12
Yes
Yes


octanone
−18
Yes
Yes


butyl acetate
−28
Yes
Yes


pentyl acetate
−15
Yes
Yes


hexyl acetate
−12
Yes
Yes


heptyl acetate
−8
Yes
Yes


octyl acetate
−15
Yes
Yes


butyric acid
−94
No
Yes


pentanoic acid
−26
Yes
Yes


hexanoic acid
−16
Yes
Yes


heptanoic acid
−14
Yes
Yes


octanoic acid
−21
Yes
Yes


pentane
−31
No
Yes


hexane
−25
Yes
Yes


heptane
−29
Yes
Yes


octane
−34
No
Yes


2,3-butanedione
−99
No
Yes


1-octen-3-ol
−27
Yes
Yes


Ethanol
−16
Yes
Yes


3-octanol
−14
Yes
Yes


Methanol
−14
Yes
Yes


Nonanol
−12
Yes
Yes


Eugenol Methyl Ether
−9
Yes
Yes


Acetic Acid
−7
Yes
Yes


gamma-valerolactone
−5
Yes
Yes


Fenchone
−2
Yes
Yes


Isoamyl Acetate
−2
Yes
Yes


Limonene
−2
Yes
Yes


Menthol
−2
Yes
Yes


(E)2-hexenal
0
Yes
Yes


Geranyl Acetate
0
Yes
Yes


Methional
0
Yes
Yes


Eugenol
1
Yes
Yes


4-methylphenol
3
Yes
Yes


Isopropyl Alcohol
3
Yes
Yes


Carvone
4
Yes
Yes


Phenylethanone
5
Yes
Yes


Anisole
6
Yes
Yes


Benzaldehyde
6
Yes
Yes


Benzophenone
8
Yes
Yes


Citronellal
8
Yes
Yes


Geraniol
8
Yes
Yes


Ethyl Acetate
8
Yes
Yes


Methylsalicylate
13
No
Yes


Thymol
15
No
Yes


Cyclohexanone
48
No
Yes


Indole
21
Yes
Yes


2-methylphenol
24
No
Yes


methyl pyruvate
−100
No
Yes


propionyl bromide
−88
No
Yes


propionyl chloride
−73
No
Yes


propionaldehyde
−68
No
Yes


2,3-pentanedione
−55
No
Yes


2-heptanol
−39
No
Yes


2-(propylamino)-ethanol
−39
No
Yes


butyryl chloride
−39
No
Yes


propionic acid
−32
No
Yes


2-methyl-3-heptanone
−26
Yes
Yes


3-heptanol
−16
Yes
Yes


4-(methylthio)-1-butanal
−15
Yes
Yes


4-hydroxy-2-butanone
−11
Yes
Yes


2,5-dimethylthiophene
−9
Yes
Yes


6-methyl-5-hepten-2-ol
0
Yes
Yes


1,5-pentanediol
0
Yes
Yes


1-hepten-3-ol
0
Yes
Yes


3-decanone
1
Yes
Yes


pyruvic acid
2
Yes
Yes


3-nonanone
2
Yes
Yes


4-heptanone
2
Yes
Yes


2-hexanol
2
Yes
Yes


1-bromohexane
3
Yes
Yes


1-hexanethiol
3
Yes
Yes


hexylsilane
3
Yes
Yes


phenylacetaldehyde
3
Yes
Yes


1-iodohexane
3
Yes
Yes


2,4,5-trimethylthiazole
5
Yes
Yes


ethyl valerate
5
Yes
Yes


cis-2-hexene
5
Yes
Yes


3-methyl-2-pentene
5
Yes
Yes


methoxyacetone
6
Yes
Yes


1-chlorohexane
8
Yes
Yes


cis-3-hexen-1-ol
10
Yes
Yes


fluoroacetone
10
Yes
Yes


acetophenone
15
No
Yes


2-acetylthiophene
31
No
Yes


pyridine
99
Yes
Yes


thiazole
100
Yes
Yes


2-ethyl-3,5-dimethylpyrazine
8
Yes
Yes


2,5-dimethylpyrazine
26
Yes
Yes


pyrazine
−8
Yes
Yes


naphthalene
−14
Yes
Yes









Optimized descriptors were calculated from the CO2 neuron activity dataset 1. As activities for the odors have been averaged across the top 2 responders of the 4 species, only a single set of descriptors were optimized representing CO2 responsive neuron activity. Molecular descriptors for this class of neuron was optimized using the same method as described above. To better visualize how well each Or-optimized descriptor set grouped CO2 responsive neuron activators, all 78 compounds were clustered by distances calculated using the optimized descriptor sets. As seen in previous examples, highly active ligands clustered tightly for each Or. (See, e.g., FIG. 19).
Optimized descriptors were calculated from the CO2 neuron activity dataset 2. As activities for the odors have been averaged across the top 2 responders of the 4 species, only a single set of descriptors were optimized representing CO2 responsive neuron activity. Molecular descriptors for this class of neuron was optimized using the same method as described in above. To better visualize how well each Or-optimized descriptor set grouped CO2 responsive neuron activators, all 104 compounds were clustered by distances calculated using the optimized descriptor sets. As seen in previous examples, highly active ligands clustered tightly for each Or. (see, e.g., FIG. 20).
Table 7 shows optimized descriptor sets calculated for CO2 activator set 1. The table shows the optimized descriptor subset calculated from activator dataset 1 as described in FIGS. 1-4 and 21. Optimized descriptor occurrences, symbol, brief description, class, and dimensionality are listed. Descriptors are listed in ascending order of when they were selected into the optimized set. Weights indicate the number of times a descriptor was selected in an optimized descriptor set.














symbol
breif description
class
dimensionality
occurrence



















HNar
Narumi harmonic topological index
topological descriptors
2
1


R3v+
R maximal autocorrelation of lag 3/weighted by
GETAWAY descriptors
3
4



atomic van der Waals volumes


HATS3m
leverage-weighted autocorrelation of lag 3/weighted
GETAWAY descriptors
3
1



by atomic masses


Mor13p
3D-MoRSE - signal 13/weighted by atomic
3D-MoRSE descriptors
3
1



polarizabilities


ISH
standardized information content on the leverage
GETAWAY descriptors
3
2



equality


P1s
1st component shape directional WHIM index/
WHIM descriptors
3
1



weighted by atomic electrotopological states


R4e+
R maximal autocorrelation of lag 4/weighted by
GETAWAY descriptors
3
1



atomic Sanderson electronegativities


nRCHO
number of aldehydes (aliphatic)
functional group counts
1
2


JGI2
mean topological charge index of order2
topological charge indices
2
2


E1u
1st component accessibility directional WHIM index/
WHIM descriptors
3
2



unweighted


MATS5m
Moran autocorrelation - lag 5/weighted by atomic
2D autocorrelations
2
1



masses


STN
spanning tree number (log)
topological descriptors
2
2


DISPe
d COMMA2 value/weighted by atomic Sanderson
geometrical descriptors
3
1



electronegativities


B06.C.O.
presence/absence of C—O at topological distance 06
2D binary fingerprints
2
1


X4A
average connectivity index chi-4
connectivity indices
2
4


JGI3
mean topological charge index of order3
topological charge indices
2
1


De
D total accessibility index/weighted by atomic
WHIM descriptors
3
2



Sanderson electronegativities


Mor25u
3D-MoRSE - signal 25/unweighted
3D-MoRSE descriptors
3
1


nRCOX
number of acyl halogenides (aliphatic)
functional group counts
1
1


B03.O.O.
presence/absence of O—O at topological distance 03
2D binary fingerprints
2
1


nHDon
number of donor atoms for H-bonds (N and O)
functional group counts
1
1


MATS3e
Moran autocorrelation-lag 3/weighted by atomic
2D autocorrelations
2
1



Sanderson electronegativities


RBF
rotatable bond fraction
constitutional descriptors
1
1


GATS5m
Geary autocorrelation - lag 5/weighted by atomic
2D autocorrelations
2
1



masses


C.008
CHR2X
atom-centred fragments
2
1


Mor13v
3D-MoRSE - signal 13/weighted by atomic van der
3D-MoRSE descriptors
3
1



Waals volumes


R6u.
R maximal autocorrelation of lag 6/unweighted
GETAWAY descriptors
3
1









Table 8 shows optimized descriptor sets calculated for CO2 activator set 2. The optimized descriptor subset calculated from activator dataset 2 as described in FIGS. 1-4 and 21. Optimized descriptor occurrences, symbol, brief description, class, and dimensionality are listed. Descriptors are listed in ascending order of when they were selected into the optimized set. Weights indicate the number of times a descriptor was selected in an optimized descriptor set.














symbol
breif description
class
dimensionality
occurrence



















N.075
R—N—R/R—N—X
atom-centred fragments
2
1


R3v.
R maximal autocorrelation of lag 3/weighted by atomic
GETAWAY descriptors
3
1



van der Waals volumes


H.049
H attached to C3(sp3)/C2(sp2)/C3(sp2)/C3(sp)
atom-centred fragments
2
1


nRCHO
number of aldehydes (aliphatic)
functional group counts
1
1




constitutional


nN
number of Nitrogen atoms
descriptors
1
1


ISH
standardized information content on the leverage
GETAWAY descriptors
3
1



equality


EEig07d
Eigenvalue 07 from edge adj. matrix weighted by dipole
edge adjacency indices
2
1



moments


piPC04
molecular multiple path count of order 04
walk and path counts
2
1


MATS4e
Moran autocorrelation - lag 4/weighted by atomic
2D autocorrelations
2
1



Sanderson electronegativities


ESpm14d
Spectral moment 14 from edge adj. matrix weighted by
edge adjacency indices
2
1



dipole moments


Mor12m
3D-MoRSE - signal 12/weighted by atomic masses
3D-MoRSE descriptors
3
1









Table 9 shows the top 500 predicted compounds for CO2 activator set 1. The top 500 predicted compounds for predictions made from activator dataset 1.













SMILES Structures
Distance
SMILES Structures
Distance


















c1ccncn1
2.033459
CN(C)CCc1cccnc1
3.487232


Cn1cncc1
2.170379
OCCCCCNCc1ccncc1
3.491708


C1═NC═CN1
2.297704
OCCC(C)c1ccncc1
3.499793


c1ncc[nH]1
2.297704
Cc1cncc(c1)c1ccccc1
3.508292


Cc1cnc[nH]1
2.409222
C═COCCNCCc1ccccn1
3.512242


OCCCCc1ccncc1
2.551873
CCOCCc1ccccn1
3.515488


Cn1cccn1
2.646606
O1CCC(CC1)c1ccncc1
3.517108


CCCCCc1ccncc1
2.66955
C#Cc1ccncc1
3.522205


Cn1cncn1
2.683402
NNCc1cccnc1
3.522389


N1N═CC═C1
2.768945
Cc1cncc(C)c1
3.522667


c1ccn[nH]1
2.768945
C1CCC(CC1)c1ccncc1
3.522853


c1ccnnc1
2.785637
CCCOCCc1ccccn1
3.526678


CC(O)/C═C\c1cccnc1
2.801027
CCCn1cncc1
3.533624


CC(C)CCCc1ccncc1
2.810277
OCCNCc1ccccn1
3.533863


Cc1ncc[nH]1
2.819446
CCCCCC1═NC═CC═C1
3.534064


Cc1c[nH]cn1
2.821426
CCCCCc1ccccn1
3.534064


CCCCC/C═C/c1cccnc1
2.848973
CCCCC(CC)CCc1ccncc1
3.541254


C═COCCNCCc1ccncc1
2.856722
CCCC(C)Nc1ccccn1
3.547778


Cc1c[nH]nc1
2.864148
n1ccc(cc1)CNC1CCCC1
3.549867


CCCc1ccncc1
2.867075
c1ccc(CNCc2ccc[nH]2)cn1
3.555656


CCCCc1ccncc1
2.88623
c1ccc(cc1)\C═C/c1ccncn1
3.55859


CCCCCCCCc1ccncc1
2.889588
CN(C)CCc1ccncc1
3.561808


c1cnn[nH]1
2.890173
c1ccc(cc1)c1cnc[nH]1
3.574979


CC(C)CNCc1ccncc1
2.891618
CC1OCCC(C1)c1ccncc1
3.577056


Cc1ccn[nH]1
2.894352
CNCCc1cccnc1
3.577696


CNCC/C═C/c1cccnc1
2.904657
N1CCC(CC1)Cc1ccncc1
3.581065


NCCCc1ccncc1
2.919508
CCOCCCNCc1ccncc1
3.582788


CCCCCCNCc1ccncc1
2.930239
OCCC(N)c1ccncc1
3.58383


OCCCc1ccncc1
2.963816
C#CC/N═C\c1ccccc1
3.584109


CC(O)CCCc1ccncc1
2.972694
CC(N)Cc1cccnc1
3.586069


C/C═C/CCc1ccccn1
2.992266
NNc1ccncc1
3.589658


CCCNCc1ccncc1
2.995958
c1ccc(cc1)Cc1ccncc1
3.589705


CCCCCCCCn1ncnc1
2.997064
C1CCC(CN1)Cc1cccnc1
3.59285


CC1═CSC═N1
3.028959
NCCCc1ccccn1
3.592971


Cc1cscn1
3.028959
C═Cn1cncc1
3.595285


NCCNCCc1ccncc1
3.055465
OCCCCCNCc1ccccn1
3.598167


Cc1n[nH]cn1
3.057359
OC1CCN(CC1)Cc1ccncc1
3.599777


C1CCC(CC1)Cc1ccncc1
3.067562
NNc1cccnc1
3.608748


OCCNc1ccncc1
3.097835
c1ccc(cn1)c1ccccc1
3.609145


N1CCC(CC1)Cc1cccnc1
3.125454
c1ccc(cc1)Cc1cccnc1
3.611841


CCCCCCc1cccnc1
3.13767
COc1cncc(OC)n1
3.612227


OCCCCCNCc1cccnc1
3.138696
COCCc1nccc(C)c1
3.615322


OCCNCCc1ccncc1
3.151658
CCCCCCn1cncc1
3.618375


CCCc1cccnc1
3.161792
CCCCOc1ccncc1
3.618896


CCCCNCc1ccncc1
3.163311
CC(N)CCn1cccn1
3.624274


Nc1cncs1
3.164302
C1CCC(CC1)CCCc1ccncc1
3.630432


CCCCc1cccnc1
3.17431
c1ccc(cc1)CCCc1ccncc1
3.635746


OCCNCc1cccnc1
3.190109
C═CCc1ccccn1
3.6393


Cc1csnc1
3.190481
CN(N)c1ccncc1
3.642521


Cc1nccs1
3.19845
CCCCCCCCn1ccnc1
3.647362


CNCCCNCc1ccncc1
3.22583
C1CCC(CC1)NCc1ccncc1
3.652448


CNCCc1ccncc1
3.232084
C1CCC(CC1)Nc1cccnc1
3.660288


OCCNCCNCc1ccncc1
3.2344
CCCCCOc1cccnc1
3.663809


CCC(CO)NCc1ccncc1
3.238621
OCCc1cccnc1
3.665454


C═CCNCc1ccncc1
3.239061
n1ccc(cc1)C1CNCC1
3.668676


CCC(C)NCc1ccncc1
3.249082
CCCCCc1cccnc1
3.669409


CCCCN(C)Cc1ccncc1
3.262013
CC(C)CCNCc1cccnc1
3.675901


CC(C)Cc1ccncc1
3.276179
CC(C)NCc1ccncc1
3.682412


C═CCNc1cccnc1
3.284046
Oc1cncc(O)c1
3.684342


OCCCNCc1ccncc1
3.28714
CCCCNCc1ccccn1
3.685337


CC(C)Cc1cccnc1
3.290287
c1ccc(cc1)\C═C/c1ncccn1
3.688202


CCc1cncc(C)c1
3.296279
C1CNC(C1)Cc1cccnc1
3.693127


C1CCC(NC1)Cc1ccncc1
3.299132
CNc1ccccn1
3.69627


OCCCc1cccnc1
3.300138
CCn1cncc1
3.697594


CCC(CO)NCc1cccnc1
3.301803
CCNCc1ccccn1
3.69802


n1ccc(cc1)CCn1cccc1
3.309288
Cc1ccc(cn1)c1ccccc1
3.701697


CN(C)/N═C/c1ccccn1
3.309846
CCCCC(CCCC)c1ccccn1
3.705387


CCOCCc1ccncc1
3.313444
COc1cc(O)cnc1
3.705406


OCCOCCNCc1cccnc1
3.324462
CC(C)OCCCNCc1ccncc1
3.706372


CCNCc1ccncc1
3.332404
CNc1ncccn1
3.706648


OCCNCc1ccncc1
3.355513
NCCc1cccnc1
3.710193


CC(N)Cc1ccncc1
3.358047
CCc1ccccn1
3.71041


C═Cc1ccncc1
3.364881
CCCCc1cnc(N)nc1
3.710859


Nc1nccs1
3.369255
c1ccc(cc1)CCc1ccncc1
3.713706


OCCC(CCO)c1ccncc1
3.38942
Cc1ccc(cc1)c1ccncc1
3.71477


NCCc1ccncc1
3.396504
C/N═C/c1ccccc1
3.716001


C1COC═N1
3.400156
CCO/C═N/c1ccccc1
3.717426


CC(C)CCCc1ccccn1
3.404779
CCO/C═N\c1ccccc1
3.720444


C═Nc1ccccc1
3.40591
COCCNCc1cccnc1
3.721211


CC(O)/C═C/c1ccncc1
3.408198
CN1C═CC═C1
3.721854


c1ccns1
3.419366
Cn1cccc1
3.721854


CN(C)CCNCc1cccnc1
3.422927
Nn1cccc1
3.722507


COCCCNCc1ccncc1
3.423191
CN(C)CCN(C)Cc1cccnc1
3.726622


CN(C)CCCc1ccncc1
3.429515
CCCCC(N)c1cccnc1
3.728514


CN(C)CCCNCc1ccncc1
3.43432
CC(O)/C═C/c1ccccn1
3.728985


Nc1ccn[nH]1
3.442882
n1ccc(cc1)CNC1CC1
3.733392


C#CCNCc1ccncc1
3.448445
Nc1cncc(N)c1
3.735731


n1ccc(CCNC2CC2)cc1
3.449431
c1ccc(cc1)CNc1cccnc1
3.739868


CNCc1ccncc1
3.449774
CC(C)CNc1ncccn1
3.74062


CC(C)Nc1cccnc1
3.450399
C/C═C/CC(C/C═C/C)c1ccncc1
3.743814


N1CCC(CC1)c1ccncc1
3.45071
c1cnc(nc1)c1ccccc1
3.749999


OCCNCCNCc1cccnc1
3.461336
CCCCCn1cncc1
3.751592


OCCOCCNCc1ccncc1
3.462592
COc1ncccn1
3.752011


CCCCNc1ncccn1
3.463639
CCCCC(CCCC)c1ccncc1
3.753138


OCCC(═C)c1ccncc1
3.464044
Nc1ccccc1CCc1cccnc1
3.75703


C═CCCCCCCCC/C═C/c1cccnc1
3.46455
CCC1═NC(═CN═C1)C
3.758666


c1ccc(cc1)\C═C/c1ccncc1
3.469761
NCCc1ccncn1
3.763006


c1nnn[nH]1
3.470708
CC(N)Cc1ccccn1
3.763389


Cc1ccc(\C═C/c2ccncc2)cc1
3.478825
[nH]1nc[nH]nc1
3.767631


c1ccc(nc1)CCc1ccccn1
3.4792
OCCCn1cncn1
3.770534


Nc1cccc(\C═C/c2ccncc2)c1
3.772142
Cc1ncc(N)nc1
3.921502


Cc1cc[nH]n1
3.772838
c1ccc(cc1)c1ccccn1
3.92194


C═C/C═C\CCCCCCCCO
3.778543
C1CCC(NC1)CCn1cncc1
3.922694


CC1CCCCN1Cc1ccncc1
3.779824
Nc1ncc(s1)c1ccccc1
3.924187


C1CCCC(CCC1)NCc1ccncc1
3.781268
CCCCOCCCNCc1ccncc1
3.924987


CCCNCc1ccccn1
3.781866
CCCCCCCCOCn1cncn1
3.92499


CC(C)CCc1nccnc1
3.785656
CCCCCCCCNCc1cccnc1
3.927536


CCOCn1cncn1
3.7873
NCCCC(N)c1ccncc1
3.928649


c1ccc(nc1)Cn1cccc1
3.788948
NCCNCCNCCc1ccncc1
3.929749


CCN(CC)Cc1ccccn1
3.789854
N1CCC═CC1
3.929961


COCCNCc1ccccn1
3.792512
OCCNCCNCc1ccccn1
3.930056


n1ccc(nc1)c1cscc1
3.804935
OCCC(CC)c1ccncc1
3.933383


NCCCc1cccnc1
3.805856
Oc1cnc(nc1)c1ccccc1
3.934528


C═Cn1cncn1
3.807139
Cn1cnnn1
3.93691


Nc1cnccc1c1ccccc1
3.812096
Cn1nncn1
3.939852


Cc1ccc(CNCc2cccnc2)s1
3.812097
C1CNC(CO1)c1cccnc1
3.940647


CCC(N)c1ccncc1
3.812101
CCCCCNCc1ccncc1
3.942016


CC1═CN═CC(C)═N1
3.815402
Cc1ncnc(N)c1
3.94231


Cc1cncc(C)n1
3.815402
CCCCOc1ccccn1
3.94276


CCn1cncn1
3.815701
CCCCC(O)Cc1ccccn1
3.943205


CCC1OCCC(C1)c1ccncc1
3.816733
C═CCn1cccn1
3.943451


C1═CC═CS1
3.817309
OCCCCCCCCCCCc1ccccn1
3.943807


c1cccs1
3.817309
C1CCCC(CCC1)Nc1ncccn1
3.947406


CCCCCCCNCc1ccccn1
3.821691
N1CCC(CC1)Cn1cncc1
3.948581


Cc1ccc(\C═C/c2ccccn2)cc1
3.822084
NCCCC(N)c1cccnc1
3.949274


CCC(CC)c1ccncc1
3.824604
C1NCC(C1)Cc1cccnc1
3.952809


Cc1nccnc1CCO
3.829076
CC(C)/N═C/c1ccccc1
3.953581


n1ccc(cc1)C1CCCN1
3.82945
CCCCCCCCCCCCCc1ccncc1
3.954487


COCCCNCc1ccccn1
3.829925
COCCNCc1cnn(C)c1
3.956375


CC1═NC═CN═C1CC
3.82998
O═CNc1ccccn1
3.95891


CCc1nccnc1C
3.82998
c1ccc(cn1)c1ccc[nH]1
3.961611


OCCCc1cnn(C)c1
3.831839
c1ccc(cn1)Cn1cccc1
3.962207


NNCCc1ccncc1
3.836858
Cc1cc(CC═C)ncc1
3.964769


CC1OC(C)CC(C1)c1ccncc1
3.837242
C═Cc1ccccn1
3.966734


OCCc1cncc(C)c1
3.837984
c1ccc(cc1)c1cncs1
3.967009


Nc1c[nH]cn1
3.838209
C═CCn1cncc1
3.967641


N1CCC(CC1)CCn1cncc1
3.838228
C1CCCC(CCC1)NCc1cccnc1
3.96989


Cc1ccc(cc1)c1nccs1
3.838297
Nc1ccc(cc1)Cc1cccnc1
3.971588


Nc1ncc[nH]1
3.838354
Cc1nccnc1CCCC
3.972325


Cc1cnc(nc1)c1ccccc1
3.839282
COc1ccccn1
3.972372


CCOc1ccncc1N
3.839786
CCCCc1ccccn1
3.972673


Nc1ccc(CCc2ccncc2)cc1
3.840154
Cc1ccc2cnccc2c1
3.974412


CCCCNCc1cccnc1
3.840485
Cc1ccnc(c1)c1ccccc1
3.975783


CCc1ccc(C═C)nc1
3.844342
c1csc(c1)\C═N/N═C/c1cccs1
3.977461


n1ccc(cc1)C1CC1
3.846694
Cc1cccnc1
3.97977


CC(C)CNCc1ccccn1
3.847544
NCCCCc1ccccn1
3.980221


c1ccc(cc1)CCCc1cccnc1
3.848581
OCCNCc1cnn(CC)c1
3.98241


NCCC(CCO)c1ccncc1
3.849054
C1CCC(CN1)COc1cccnc1
3.982921


C1CCC(CC1)Nc1nccs1
3.851878
c1ccc(cc1)\C═C/c1ccccn1
3.983757


CCC1═NC═CN═C1CC
3.853768
CN(C)/C═N/c1ccccc1
3.984609


CCc1nccnc1CC
3.853768
OCC(C)Cn1cccn1
3.985181


CCCCn1cncc1
3.855689
C═Cc1ccc(C)nc1
3.98793


c1ccc(nc1)c1ccccn1
3.857047
Cc1cncs1
3.988318


CCCc1ccccn1
3.858559
COCn1cncc1
3.989514


n1ccc(cc1)CCNc1ccccc1
3.858864
CCC(C)n1cccn1
3.990983


n1ccc(cc1)C1CCC═CC1
3.859078
CCCCOc1ccc(CNCc2cccnc2)cc1
3.99149


Oc1cccs1
3.861787
OCn1cncc1
3.9916


C1CCNC(CC1)c1ccncc1
3.862012
CCC(C)NCc1cccnc1
3.992449


C1CCC(CNCCCn2cncc2)CC1
3.862902
CC(C)NCc1ccccn1
3.994445


c1ccc(nc1)Nc1ccccn1
3.864257
c1ccc(cc1)COCc1cncs1
3.994736


Nc1nccc(c1)c1ccccc1
3.86608
CCCN(CC1CC1)Cc1ccncc1
3.995188


n1ccc(cc1)Cn1cccc1
3.867767
Cc1ccccc1Cc1cccnc1
3.996041


NCCCNc1cccnc1
3.869999
CC(C)c1ccccn1
4.00194


OCCc1ccncn1
3.872425
CCCCCCNCc1cccnc1
4.002367


CCCc1ccc(O)cn1
3.873872
COc1ncc(N)cn1
4.00385


Nc1ccc(cn1)c1ccccc1
3.87394
C1CCc2nccnc2C1
4.004779


Nc1nncs1
3.876929
c1ccc(cc1)NCc1ccncc1
4.005823


c1ccc(cc1)c1c[nH]cn1
3.877242
C═Cc1nccnc1C
4.006881


c1ccc(cc1)Cc1ccccn1
3.881093
O═Cc1cnc(s1)c1ccccc1
4.007171


OCCCc1cnn(CC)c1
3.882615
CC(N)c1ccncc1
4.009614


c1ccc(cc1)c1ncc[nH]1
3.883167
CCCC(CCC)c1ccccn1
4.012339


c1ccc(cn1)c1n[nH]cc1
3.885425
OCC1CCN(CC1)Cc1ccncc1
4.012772


NCCCCn1cncc1
3.885956
Cc1cc(C═O)cnn1
4.013137


c1ccc(nc1)\C═C/c1cccnc1
3.886201
CNCCCC(O)c1cccnc1
4.013331


COCCc1ccccn1
3.886334
NCCCCCc1cnc[nH]1
4.014127


C12═CC═CC═C1N═CC═N2
3.889099
COCC(NC)c1ccccn1
4.016933


c1ccc2nccnc2c1
3.889099
CCCCc1cnccn1
4.017648


c1ccc(cc1)c1ccn[nH]1
3.890727
CCCCCCC(C)Cc1ccncc1
4.017949


c1ccc(nc1)NC1CCCC1
3.892768
O1CCN(CC1)c1ccncc1
4.018166


OCCNCc1cnn(C)c1
3.893737
C1N═Cc2ccccc2C═C1
4.018324


CCOc1ccccn1
3.901646
CCCN1CCC(CC1)NCc1ccncc1
4.01907


Cc1ccnc(C)c1
3.902494
NCCNc1cccnc1
4.019396


COCc1ccccn1
3.907315
CCCCCn1ccnc1C
4.020339


NCCc1cncs1
3.909061
OCCc1ccncc1
4.023571


Nc1cccc(c1)c1ccncc1
3.90929
C═CCn1cncn1
4.024219


CCCCCCCCCCCCn1ccnc1
3.910022
n1ccc(cc1)C1CCCCN1
4.024864


CCC1CCC(CC1)NCc1ccncc1
3.910398
OCCNc1ccncc1N
4.025311


CCCCCCn1cncc1C
3.911132
CCCCN(CCCC)c1ccncc1
4.025433


n1ccc(cc1)\C═C/c1ccccn1
3.91129
Cc1ncc(C)cn1
4.027625


Oc1cnsn1
3.912538
CC(═N)NCCc1ccncc1
4.028513


CCC1═NC═CN═C1
3.914359
CC(C)Cn1cccn1
4.028995


CCc1cnccn1
3.914359
c1ccc(nc1)NCc1cccs1
4.030544


NCCNc1ccccn1
3.914758
C═Cc1cccnc1
4.031618


c1cnc(nc1)NC1CCCC1
3.915571
CCC/N═C/c1ccccc1
4.032101


CCC(CC)c1ccccn1
3.915951
COc1ncc(O)cn1
4.032331


Cc1ccc(CNc2cccnc2)cc1
3.917527
Nc1ccc(cc1)Cc1ccncc1
4.032768


OCCC(CCO)c1ccnc(C)c1
3.919641
CC1OC(C)CN(C1)c1ccncc1
4.034999


Oc1ccccc1\C═N/c1nccs1
3.919723
CCN(CC)CCc1ccccn1
4.035536


COCCNCc1ccncc1
3.920298
n1ccc(cc1)CNCc1cccs1
4.037681


OCc1cncn1CC
3.920528
CC1═NC═CN═C1OC
4.038081


O═CNc1nnc(CC(C)C)s1
4.075545
COc1nccnc1C
4.038081


Cc1cccnc1c1ccccc1
4.075705
CCN(CC)CCc1ccncc1
4.038201


C1CCC(CN1)Oc1ccncc1
4.075903
OCC1CCN(CC1)Cc1cccnc1
4.038372


N1CCC(CC1)CCc1ccccn1
4.077268
COC1═NC═CN═C1CC
4.03881


c1ccc(nc1)c1ccncc1
4.078491
COc1nccnc1CC
4.03881


CCCCc1ccc(C═O)cc1
4.07867
CCn1cccn1
4.039322


c1ccc(nc1)C1CCOCC1
4.080083
CCOC(OCC)Cn1cccn1
4.039711


c1ccn2nccc2n1
4.081331
N1CCC(CC1)Cc1ccccn1
4.0407


CC(CC)n1cncn1
4.082857
N1═CNCCN═CNCC1
4.040829


OC1CCN(CC1)Cc1cccnc1
4.083942
NCCCn1cncn1
4.041459


Cc1nncc(c1)c1ccccc1
4.084906
Cc1ccc(cc1)c1cncnc1
4.043691


CCC/C═N/Nc1ccccc1
4.087764
CCOc1cnccc1OCC
4.044495


C/C═C/CC(C/C═C/C)c1ccccn1
4.089417
NCC(C)c1ccncc1
4.044646


COCC(N)c1ccccn1
4.0913
CN(C)Cc1ccccn1
4.045576


CC/C═C\C/C═C\C/C═C\CO
4.092416
OCCCNC(C)c1ccncc1
4.047575


CCC(N)Cn1cncc1
4.092784
C═CC1═CN═CC(C)═N1
4.048223


CC(N)Cn1cncc1
4.093284
NCCCOc1cccnc1
4.048408


OCCCNCc1ccccn1
4.094216
Cc1ccc(cc1)c1ccccn1
4.048665


C/N═C/c1ccc(cc1)C(C)C
4.094231
Cc1ccnc(c1)c1nccc(C)c1
4.049527


C[C@H](O)c1ccncc1
4.094662
CCCCC(CCCC)c1cccnc1
4.049925


CC(O)c1ccncc1
4.094662
C1CNC(C1)Cn1cccn1
4.052425


CC(═C)Cc1ccccn1
4.095187
CC(C)CNc1ccccn1
4.053681


CN1CCN(CC1)Cc1cccnc1
4.096565
Nc1cnc(nc1)c1ccccc1
4.053727


c1csc(c1)\C═N/N═C\c1cccs1
4.097744
N1CCCN(CC1)Cc1ccncc1
4.053805


C═CCNc1nccs1
4.098084
CC(C)Nc1ccncc1N
4.055925


CC(C)CC(C)c1ccccn1
4.098869
n1ccc(nc1)c1cccs1
4.056535


CCCCNc1ccncc1N
4.100089
CCOc1ccc(CNCc2cccnc2)cc1
4.056591


OC1CCCN(C1)Cc1cccnc1
4.100969
NCCCCCC(N)c1ccccn1
4.056898


OCc1ccc(CO)cn1
4.104339
CCC(NC)c1ccccn1
4.056979


COCCn1cncc1CO
4.104345
NCc1ccc(s1)c1ccncc1
4.058566


OCc1cnc[nH]1
4.104405
Cc1cccc(c1)c1cncnc1
4.058849


OCCC/N═C\c1cccs1
4.106196
CC(C)Oc1nccnc1C
4.059391


C1CCN(C1)c1nccs1
4.109143
Nc1n[nH]cn1
4.059558


C1CCC(NC1)CCc1ccccn1
4.11042
Nc1cncc(O)c1
4.061363


CCCn1cc(N)cn1
4.111561
NC1CCCN(C1)Cc1cccnc1
4.061742


Cc1c[nH]cc1
4.111597
COCn1cncn1
4.062793


Nc1ccc(CCc2cccnc2)cc1
4.113066
Nc1ccc(cc1)c1ncsc1
4.064229


C1C═CC═C1
4.113191
CCOc1cnc(C)cn1
4.06474


OCC(N)c1cccnc1
4.113658
Cc1nccnc1CCC
4.066969


c1ccc2ccncc2c1
4.113947
Nc1cc(CO)cnc1
4.067234


c1ccc(CNCc2cccs2)cn1
4.114262
OCCCc1ccnc(C)c1
4.068903


CC(N)Cn1cncn1
4.115272
c1ccc(cc1)CNc1ccccn1
4.069396


CCCCn1nccc1C
4.115424
CCCCNc1nccs1
4.072269


CCC(CO)NCc1ccccn1
4.118634
Cc1ccc[nH]1
4.072803


OCCn1nccc1N
4.119024
Nc1cnc(C)nc1
4.073371


CCCCn1ccnc1C
4.119754
CCCCCCCNCc1ccncc1
4.073913


Nc1ccccc1c1ccncc1
4.121111
Nc1ccc(nc1)c1ccccc1
4.073915


O═Cc1ccnn1CC
4.121672
CC1═COC═C1
4.074861


OCCCCCCc1cccnc1
4.122515
c1cnc2cccnc2c1
4.074967


CC1═NC═C(CC)C═C1
4.124326
C1CNC(C1)Cn1cncn1
4.075254
















TABLE 10







Top 500 predicted compounds for CO2 activator set 2. The top 500 predicted


compounds for predictions made from activator dataset 2.










SMILES Structures
Distance
SMILES Structures
Distance













O═C1CCNCC1
1.66855
O═C1CNCC1
5.644213


O═C1CCCCCN1
2.03737
C1COC═N1
5.670562


O═C1CCCCCN1
2.03737
S═C(NCCc1ccccc1)NC(C)(C)C
5.700199


O═C1NCCCC1
2.16724
N#CC(═C1CCCCCCCCCCC1)C#N
5.701419


O═C1CCCCN1
2.16724
COC1═CC═CCC1
5.71922


O═S1CCNCC1
2.17659
CC(C)(C)NCc1ccccc1
5.720564


O═C1NCCOCC1
2.43129
CC(C)CC(═O)c1cccnc1
5.7212


O═C1CCNCCC1
2.50405
NC1CCCCC1
5.72578


O═C1CCC═CCC1
2.67564
CCc1cncc(C)c1
5.744045


Oc1ccncn1
2.97767
CNC1CCCC1
5.745127


C1═CC═CS1
2.99768
OC1CCNCC1
5.749396


c1cccs1
2.99768
CC(C)═C[C@H]1C[C@@H](C)CCO1
5.751913


O═C1CCOCC1
3.06572
COC1CCC═C1
5.761583


CC1CCOCO1
3.1695
CC(C)OC(═O)C1CCCCC1
5.765544


O═S1CCCCC1
3.34159
CC(═CCOCC1CCCNC1)C
5.773864


O═C1CCCCCC1
3.37411
C1CCCSC1
5.776573


Cc1ccncc1
3.44412
C1CCSCN1
5.78245


O═C1CCCNC1
3.53322
O═Cc1c[nH]cn1
5.783256


O═C1CCCCO1
3.6966
CCOC(═O)N1CCCCCC1
5.783974


NC1CCOCC1
3.73016
C1COC═CC1
5.794384


O═CN1CCCC1
3.75356
COC1═CCCCC1
5.795498


O═C1CCCCC═C1
3.83862
O═C(OC(C)(C)C)c1cccnc1
5.795791


NC1CCSCC1
3.88042
OC1CCCCO1
5.807573


CC1═CC═NC(═O)C1
3.88476
OC1CCCCCC1
5.823095


O═C1C═CCCC1
3.97735
CCCCCC#Cc1ncccc1C#N
5.8237


O═C1CCCC═C1
3.97735
CC(═O)CC(═O)N1CCCCCC1
5.82382


OC1CCSCC1
3.9899
O═C(CC(C)(C)C)c1ccccc1
5.828928


C1CCOCO1
4.04859
CC(C)(C)OCc1ccccc1
5.830675


O═C1CCC═CC1
4.05289
CC1(O)CCCCCC1
5.846164


N1CCC═CC1
4.09099
O═c1cc[nH]cc1
5.850777


C1C═CC═C1
4.09349
C/C═C/C1OCCO1
5.852582


O═S1(═O)CCNCC1
4.25706
CS(═O)(═O)N1CCCCCC1
5.852941


CC1CCOC(═O)C1
4.31492
Cc1ncc(C)cn1
5.872505


O═S1(═O)C═CCCC═C1
4.35802
CC(C)(C)NCC#Cc1ccccc1
5.872605


O═c1cccn[nH]1
4.38874
CCCOc1cccc(C)c1
5.874069


CC1CCNCC1
4.39789
C1NNC═C1
5.876098


CC1CCNCC1
4.39789
CC(═CC(═O)N1CCCCCC1)C
5.892267


c1ccncn1
4.44564
CC(C)CCOC1CCNCC1
5.894852


C1CCC═COC1
4.46091
CCCN1CCCCC1
5.899168


c1ccnnc1
4.50924
O═Cc1cccs1
5.902546


O═C1CCCC(═O)N1
4.53618
CC(═C)Cc1ncccc1C
5.90473


O═S1(═O)CCCCC1
4.55931
CC1(C)CCNCC1
5.91625


O═C1CNCCN1
4.60068
O═C1CCCCCCCCC(═O)OCCCCO1
5.927301


C1═NC═CN1
4.61974
O═C1CCNC(═O)N1
5.933254


c1ncc[nH]1
4.61974
OC1CCNCCC1
5.937124


O═C1CNCCCN1
4.64001
O═C1COCCN1
5.944039


CN1CCOCC1
4.6583
O═C(NC(C)(C)C)c1ccncc1
5.947731


O═C1NCCCO1
4.721
CC(O)CC#CCN1CCCCC1
5.948158


O═S1(═O)CCOCC1
4.79345
O═C1CCCC1
5.94834


O═C1CCCCC(═O)N1
4.79537
O═C1CCCC1
5.94834


CN1CCC═CC1
4.91248
C1CC═CC1═O
5.95686


O═C1C═CC═CO1
4.9195
OCCC#CCN1CCCCC1
5.957687


N1N═CC═C1
4.94377
C═CC1OCCO1
5.959976


c1ccn[nH]1
4.94377
CN1CCCCCC1
5.962768


C1CCCOC1
4.97412
Cc1cc[nH]n1
5.96375


O═C1CCCC(═O)C1
5.01506
O═C1NCCNCC1
5.967599


Cc1ccncn1
5.03254
CC(C)═CC1C═C(C)CCO1
5.971966


c1nnn[nH]1
5.0834
CC(═C═CSc1ccccc1)C
5.975644


CC1═NNCC1
5.12159
CCOCCCNC(═O)n1cncc1
5.979802


N1CCCCCC1
5.12723
OCC1CCCC1
5.980761


OCc1cnn[nH]1
5.15293
C1CNCCNC1
5.984318


CC(C)CC(═O)N1CCNCCC1
5.1552
CCOC1CCCO1
5.986821


CN1CCCCC1
5.16702
CCCCOc1cccc(N)c1
5.987676


CN1CCCCC1
5.16702
CC1═CCCN1
5.994846


NCc1nnn[nH]1
5.23459
CC(═O)CC(═O)NC1CCCCC1
5.995065


c1ccns1
5.24232
O═C1CC(C)(O)CCO1
5.996197


C1CNCCOC1
5.24713
O═C1OCCC(C)(O)C1
5.996197


C1CCNOC1
5.25085
CCCNC1CCCCCC1
5.996813


C1CCCS1
5.27768
OCC1COCC1
5.99948


c1cnn[nH]1
5.28134
CCCn1ccc(═N)cc1
6.007298


O═c1cn[nH]c(═O)[nH]1
5.29523
OC[C@H]1CNCC1
6.00856


O1CCCCCC1
5.29737
CC(═CC(═O)N1CCNCC1)C
6.009864


O═C1CCCC(═O)O1
5.29845
O═C1OCC[C@@](C)(O)C1
6.011353


O═c1ccnc[nH]1
5.29899
O═C1CC2CCC1CC2
6.017842


C1OCC═CCO1
5.33288
CC(C)CC(═O)N1CCCCCCC1
6.01785


O═S1(═O)CCCCO1
5.35017
CC1CC(C)CC(═O)C1
6.018673


C/C═C/C(═O)N1CCCCCC1
5.3502
O═Cc1ccc(OCCC(C)C)cc1
6.022296


CC(C)CC(═O)C1CCCCC1
5.38026
CCOC1═CCCCC1
6.030238


CNc1nnn[nH]1
5.38777
CC1CNCC(C)C1
6.031252


O═C1NCCCN1
5.41177
O═S1(═O)C═CC═CC═C1
6.03423


CC/C═C/N1CCCCC1
5.41882
CN(C)NC(═O)c1ccccc1
6.034587


n1ccnnc1
5.42336
NC1CCNCC1
6.039425


C═CCON═C1CCCCC1
5.43999
CC(C)c1nnn[nH]1
6.044467


O═C(NC(C)(C)C)c1ccccc1
5.44125
CCCCCCCc1ccc(C#N)cc1
6.044688


C[C@H]1CNCC1
5.45426
C1CC═CC1
6.044933


CC1CNCC1
5.46662
CN1CCCC1
6.046603


NC1═NCCO1
5.469
CC(C)(C)NSc1ccccc1
6.046834


C[C@@H]1CNCC1
5.48036
CN(C)CCCNC(═O)n1cncc1
6.05255


O═C1CCCCCO1
5.51226
N#CC1═CCCCCCCCCCC1
6.058942


CCON═C1CCCCC1
5.53578
CN(C)CCC1CCNCC1
6.062633


CC(C)NC(═O)n1cncc1
5.5454
O═C1CC═CC1
6.06268


CNC1CCCCCC1
5.56246
CCC(═O)NCCCn1cncc1
6.068374


CN1CCCN(C)C1
5.60836
CCc1cnc(N)nc1
6.07402


CC(C)═CC1CC(C)═CCO1
5.60984
CC(C)═CC1OCCC(C1)C
6.075926


CC1COCC1
5.61068
CC1CCOC(C1)C═C(C)C
6.075926


O═Cc1cc[nH]n1
5.61618
CC(C)OC(═O)CCNC(═O)n1ccnc1
6.076841


CC(═C)Cc1ccccn1
5.61981
CCCn1cccc1
6.081147


CC(C)(C)/N═C/c1ccccc1
5.62608
C1Cc2[nH]ncc2C1
6.084577


O═C1CCCS(═O)(═O)CC1
5.62707
CC(C)CCNc1ccccc1
6.088477


O═C1OCCOCC1
5.62874
CCCCCCN1CCCCCC1═O
6.088796


CCCCCCCCn1ccc(═N)cc1
6.09256
CSc1ccc(cc1)C(═O)NC(C)C
6.328161


CNc1cccc(C)c1
6.098
CCCCCCCCCn1ccc(═N)cc1
6.328322


CC(═O)C1CCCCCCCCCCC1═O
6.09886
CCCNCC1CCCCC1
6.33169


CC1═CCCO1
6.1036
S═C(N/N═C/c1cccnc1)NC(C)(C)C
6.332076


CCCCCn1ccc(═N)cc1
6.11175
N#CC(═CNc1ccncc1)C#N
6.332338


CCCCc1ccc(N)cc1
6.11758
CCCCNc1ccccc1
6.3364


N#CCCOc1cc(C)cc(C)c1
6.11793
CC(C)COC1CCNCC1
6.336875


CCNC(═O)C1CCNCC1
6.12143
C1C═CCCC═C1
6.337419


Cc1cccc(NN═C(C)C)c1
6.12331
CCOC1CCCCO1
6.338426


CN(C)CCCNC(═O)c1cccs1
6.12848
CCOC(═O)C#Cc1cccc(C#CC(═O)OCC)c1
6.33956


CCC(═O)NCCC1═CCCCC1
6.12936
COC(═O)\C(═C/C#CC1CCCCC1)/C
6.340497


CC(═C)Cc1cccc(C)n1
6.13039
CC(═C)COCC1CCCNC1
6.341306


C═CCc1ccccn1
6.14194
Cc1cccc(CC═C)n1
6.342212


OC1CCCCC1
6.14367
O═C(NCCCn1cccn1)C1CC1
6.342747


OC1CCCCC1
6.14367
CCCC(═O)NC1CCN(C)CC1
6.345418


CCN(CC)C(═O)Sc1ccccc1
6.15327
OC1CCCCN1
6.345647


CCCC(═O)C1CCNCC1
6.15506
CCN(CC)CCCNC(═O)c1cccs1
6.347501


CC1CCCS1
6.15615
CC(═O)NCCCNC(═O)c1cccnc1
6.348592


C1CCN═CN1
6.1566
CCC1NCC═C1
6.348743


CCCCc1ccc(C#N)cc1
6.15777
CCC1CCCCO1
6.35009


CC(C)CCNC(═O)c1cccs1
6.16447
CN(C)/C═N/c1ccccc1
6.350651


CS(═O)(═O)N1CCNCCC1
6.16796
CSCCC(═O)Nc1nccs1
6.353907


CC1CCCN(C)C1
6.16806
CN1CCNCCC1
6.355103


O═C1CCCCC(═O)O1
6.16947
O═c1cc[nH]c(═O)[nH]1
6.3581


NN1CCCCC1
6.16961
CC(N)CCn1cccn1
6.359583


CCOC(═O)C1CCNCC1
6.16969
CCOC(═O)N1CCCCC1
6.360332


OCC#CCCOCc1ccc(OC)cc1
6.17176
O═C(Nc1ccncc1)CC(C)(C)C
6.360498


NCC1COCC1
6.17315
Cc1scc(c1)C(═O)Nc1ccncc1
6.361459


O═C(Nc1cccnc1)NC(C)(C)C
6.17625
CC(═CCOC1CCCNC1)C
6.366281


CSc1ccccc1C(═O)NCCCN1CCCCCC1
6.17718
CCCC(═O)NC1CCCCCC1
6.370017


CC(C)C1NCC═C1
6.18084
CC(C)CNCc1ccc(C)cc1
6.372029


OC(═O)C1═CCCCCCCCCC1
6.18133
CCCCOc1ccccc1C
6.373041


O═C(Nc1ccccc1)CC(C)(C)C
6.18333
CCOC1OCCCO1
6.373474


CC(═O)CC(═O)NCCCc1ccccc1
6.1847
CCC(CC)C(═O)NCCCN1CCOCC1
6.374652


CCCC(═O)N1CCSCC1
6.1868
CC1═NCCC1
6.380214


CCc1ccc[nH]1
6.18755
CCC(═O)C1CCCCC1
6.380337


O═C(NCCCn1cccn1)C1CCCCC1
6.1899
COC1═NCCCCC1
6.382579


Cc1cc(CC═C)ncc1
6.19214
COC(═O)NCCC1═CCCCC1
6.382998


O═C1CCCCC(═O)C1
6.19215
CNC(═O)NCc1ccccn1
6.383801


O═C1NC═NC(═O)C1
6.19497
CC(C)NC(═O)c1cscc1
6.38503


CC(═O)CCC═C1CCCCC1
6.1999
CNCCc1c[nH]cn1
6.389962


C═CC(C)CSc1ccccc1
6.20114
CCNC(═O)NC1CCCCC1
6.391185


CC(N)c1nnn[nH]1
6.20863
O═C1CC(═CC(═O)O1)C
6.391193


CC(C)CCNC(═O)n1cncc1
6.21052
C═CCSC(═O)NC(═O)Cc1ccccc1
6.393044


CCCC1CCCCNC1═O
6.21079
CCCC(═O)NCCCc1ccccc1
6.39395


CC(═O)NCCCNC(═O)c1cccs1
6.21297
O═S1OCCCCO1
6.393961


CC(C)CC(═O)NCCc1ccncc1
6.21313
NCCC(═O)N1CCNCC1
6.394319


CC(═O)CC(═O)NC1CCCCCC1
6.21501
CCCCN(C)Cc1ccc(OC)cc1
6.394897


CCc1nnn[nH]1
6.21826
Cn1cncc1C═O
6.395174


S1SCCC1
6.22029
CCCc1ccc(CN)cc1
6.397567


C═CC#CCN1CCCC1
6.2212
CC(═CC(═O)CCN1CCCCC1)C
6.398219


C1NCCS1
6.22176
CCOC(═O)CC1CCCNC1
6.399213


CCN(CC)C(═O)N1CCCCC1
6.22256
CC(C)(C)NNc1ccccc1
6.40262


ON1CCCCC1
6.22438
CCNC(═O)CCSc1nc(C)cc(C)n1
6.403173


CCCCCc1ccc(NC)cc1
6.23282
CC(C)NC(═S)N/N═C/c1cccnc1
6.404253


CNCC(═O)N1CCCCC1
6.23427
CC(C)CC(═O)NC1CCCCCC1
6.405964


O═C(NCC#Cc1ccccc1)NCCc1cccs1
6.23686
OC1(C)C═CCCC1
6.407874


Cc1c[nH]cc1
6.24026
CC1CCCO1
6.410532


CC(═O)NCCCNC(═O)c1ccncc1
6.24118
CCCCCCn1ccc(═CC═C(C#N)C#N)cc1
6.4135


CC(C)CC1CCCCCN1
6.24936
CCCCC1═CCCOC1
6.416425


CCN(CC)C(═S)NCCc1ccccc1
6.24969
CC(C)CCNC(═O)c1cccnc1
6.420616


CC(═O)CCCN1CCCCC1
6.25029
CC(C)/N═C(/C)\c1ccccc1
6.421906


O═C1C═CCCC═C1
6.25035
Oc1ccncc1
6.427433


CC(═CCOC1CCNCC1)C
6.25104
CCS/C═C/C#CC1(O)CCCCC1
6.428407


CCc1cccc(C)n1
6.25801
CCC(CC)C1CCCCCN1
6.428797


N#Cc1ccc(CSC2═NCCCN2)cc1
6.25841
CC1CCCC(O)C1
6.429721


O═C(O/N═C/c1ccccc1)N1CCOCC1
6.26173
Cc1c[nH]cn1
6.431754


CCCC(C)NC(═O)n1cncc1
6.26938
CCc1ccccc1OCC1CCCNC1
6.432372


N#Cc1ccc(\C═N/c2ccccc2)cc1
6.27228
CNC1CCCCC1
6.433502


CCCCCC/C═C1\CCCCC/1═O
6.27628
CCCCn1cccc1
6.434268


CNCCCOc1ccccc1C
6.28193
O═C(CCCc1ccccc1)NC(C)(C)C
6.434314


Oc1cccc(O)n1
6.2826
CC(C)CCCc1ccccn1
6.435371


OC[C@@H]1CNCC1
6.28635
CC1(C)COCCN1
6.435727


OCC1CNCC1
6.28726
S═C(NCCC1═CCCCC1)N(C)C
6.43606


O═C(NCCCc1ccccc1)C(C)(C)C
6.2907
N#CCCNc1ccccc1C
6.436422


CCc1nnc(N)nn1
6.29176
CN(C)CCCNC(═O)N1CCOCC1
6.437192


N#CC1(N)CCCC1
6.29315
CC(═C)Cc1nccc(C)c1
6.437725


CC(C)CCSc1ccc(C)cc1
6.29428
NCCOC1CCCCO1
6.437952


Cc1ccccc1OCCCCCN1CCCCC1
6.2943
CSc1ccccc1C(═O)NCCCN1CCCC1
6.43854


CC(C)CSc1ccccc1
6.2943
NCCCN1CCCCC1
6.44011


C/C═C/CCc1ccccn1
6.29577
C═COc1cccc(C)c1
6.442179


NC1CCCCCC1
6.29817
Nc1ccncc1
6.4422


O═C(NCC#Cc1ccccc1)c1cccs1
6.29973
C[C@H]1CCC[C@@H](O)C1
6.442564


CCCCCCCc1ccc(N)cc1
6.30274
CC(C)CCOc1ccc(N)cc1
6.445304


O═C(NCCCn1cncc1)C1CCNCC1
6.30333
NCCCN1CCSCC1
6.446523


Cc1ccc(C)cn1
6.30334
CN(C)c1ccccc1
6.448354


CCCCCCc1ccc(N)cc1
6.30632
CCCc1cccc(N)n1
6.448834


CCCCOCCCNC(═O)N1CCOCC1
6.30824
CCc1ccc(C═C)nc1
6.449475


CC(C)(C)COc1cncnn1
6.30882
CC(C)CNCc1cscc1
6.450718


COc1ccc(cc1)CN1CCCCCC1
6.31102
CC(C)CCCc1ccncc1
6.451931


COc1cccc(C)n1
6.31161
CC(C)NC(═O)N1CCNCC1
6.453215


C1C═CCS1
6.31378
Cc1cc(C)nc(Sc2ccc(cc2)C(═O)O)n1
6.454888


O═C(NCCCN1CCCC1)C1CCNCC1
6.31572
CC(C)(C)c1ccccn1
6.456365


Cc1ncccc1OCC1CCCNC1
6.31862
CCN(CC)C(═O)NC1CCCCC1
6.457056


CCCC(═O)NCc1ccccn1
6.32015
CCC1═NCCCN1
6.457151


CCCC1OCCS1
6.32076
CN(C)Cc1ccccn1
6.457247


CCOC(═O)\C═C\Cc1ccccc1
6.32096
CCCCSC1(C)CCCCC1
6.459166


CC(C)(C)COCC1CCCNC1
6.32318
COc1ccc(CNC(═O)CC(C)C)cc1
6.459845


NCCCOc1cccc(C)c1
6.32451
N#Cc1ccc(C#Cc2ccccc2)cc1
6.463152


O═C(NCCc1ccccc1)OC(C)(C)C
6.3266
CC(C)CCNC(═O)Cc1cccs1
6.465874


CC1(C)CCCNC1
6.46693
CCNCc1ccccc1
6.513302


Cc1n[nH]cn1
6.46712
CCCC1NCC═C1
6.514383


CCCC1═NCCO1
6.46827
CCCCCCn1ccc(═N)cc1
6.515571


COC1CNCC1
6.47129
CNC(C)Cc1ccccn1
6.515612


OC1(C)CCCC1
6.47168
CNCC(═O)N1CCCCCC1
6.51581


CC1(O)CCCC1
6.47168
O═C(NCCCN1CCCCC1)c1ccccc1
6.516288


O═C(NCCCN1CCSCC1)c1ccccc1
6.47174
CN(C)C1CCCC1
6.517077


CCC(CC)C(═O)NCCCc1ccccc1
6.47308
C1CCC(CN1)Oc1ncccn1
6.52029


CC(C)(C)NCCCc1ccccc1
6.47342
NCc1csnc1
6.521403


CCCCN1CCC(C)CC1
6.47395
CCC(═O)NCC#Cc1ccccc1
6.521463


O═C(NCCc1ccccc1)C(C)C
6.47507
CCCC1CCCS1
6.52177


O═C(NCCCn1cccn1)C1CCCC1
6.47677
OC1CCOCC1
6.522021


CCc1ccccc1Oc1ncccn1
6.4771
O═S(═O)(NCCCN1CCCCCC1)c1ccccc1
6.523599


CC(═O)CC(═O)NCCc1ccccc1
6.47888
CCN(CC)C(═O)NCc1ccccn1
6.523717


CCCCc1ccc(C═O)cc1
6.48035
O═C1C/C═C\CCCCCCCCO1
6.523937


CCCCNC(═O)N1CCOCC1
6.48123
O═C(OCCSC(═S)NC(C)(C)C)Nc1ccccc1
6.524631


N#CCCNC(═O)C1CCNCC1
6.48516
CC(C)CNC(═O)c1cscc1
6.52518


NCCCOc1ccccc1C
6.4853
O═C(NCCC1═CCCCC1)C1CC1
6.525225


CCCCNC(═O)Nc1nccs1
6.48555
CC(N)CCN1CCCCCC1
6.526511


O═S(═O)(NCCCN1CCCCC1)c1ccccc1
6.48645
CCNC(═O)N(C)Cc1ccccc1
6.528375


CCc1ccc(CNCC(C)C)cc1
6.48768
CN(C)C1CCCCC1
6.529209


CC(C)(C)NCc1cccnc1
6.49017
CC(C)CNCc1ccccc1C
6.53036


CCC(C)NC(═O)c1cscc1
6.49085
CC(C)(C)NCc1ccncc1
6.5317


CCCc1ccc(N)cc1
6.49096
CCCCNCc1ccc(CC)cc1
6.533015


CCCCN1CCCCC1
6.49113
CCCCCn1cccc1
6.533323


CC(C)OC1CCCCO1
6.49161
O═C(OC(C)(C)C)n1cncc1
6.533548


CN(C)C(═O)NC1CCCCC1
6.49217
CCNC(═S)Nc1ccc(CC)cc1
6.53399


O═C(NCC#Cc1ccccc1)c1cscc1
6.49234
OC[C@@H]1CCCN1
6.534219


CN1CCN(CC1)C(═O)CC(C)C
6.49243
CC(═C)CN1CCCCCC1═O
6.53494


CC(C)COc1cccc(N)c1
6.49256
O═C(NCCCc1ccccc1)C(C)C
6.534992


O═C(NCCCn1cncc1)n1cncc1
6.49626
OCC1CCCN1
6.535179


CC(C)C1OCC═CCO1
6.49662
CC(C)(C)N═C═NC(C)(C)C
6.535355


CCC(═O)N1CCNCC1
6.4982
CN1CCCN(CC1)C(═O)CSc1ccccc1
6.535507


CC(C)NC(═O)C1CCNCC1
6.49827
COc1ccc(cc1)C(═O)NC(C)(C)C
6.536675


CCCCN1CCC(N)CC1
6.49841
CCCS(═O)c1ccccc1
6.539592


O═C1NCCN(CC1)C(═O)OC(C)(C)C
6.49861
CC(O)CC#CCN1CCOCC1
6.539593


CC(C)(C)NCCCOc1ccccc1
6.49939
CC(═CCCC(═CCCC1═COC═C1)C)C
6.54101


O═C(Nc1cccnc1)N1CCCCC1
6.50169
N#CCCCCCC1CCCC(═O)C1
6.541903


NN1CCOCC1
6.50235
Cc1ccsc1CNCc1ccncc1
6.542274


CC(C)(C)N═C═NCc1ccccc1
6.5031
CC(═CCCC#CCCOC1CCCCO1)C
6.546339


CCCC1OCCCO1
6.5042
CCCCn1cncc1
6.547629


O═S1(═O)CCOCCS(═O)(═O)NCCOCCN1
6.50421
OC1CCCC1
6.550298


CNC1CCCCNC1
6.50462
CCC(═O)Nc1nccs1
6.552381


CC(C)C1CCCCCN1
6.50482
O═CNc1nnn[nH]1
6.552698


O═Cc1cnc(nc1)NC(C)C
6.50527
CC1(C)CCOCO1
6.554363


CN1CCCN(CC1)C(═O)CC(C)C
6.50672
CCNC(═S)NCCN1CCCCC1
6.555586


Cc1ccc(OCC2CNCC2)cc1
6.50714
CCOc1cccc(N)c1
6.555999


COC(═O)NCCCn1cncc1
6.50724
CN(C)CCCNCc1ccncc1
6.556703


Cc1ccsc1CNCc1cccnc1
6.51228
CC(═O)NCC1CCNCC1
6.556707


C1CCSCS1
6.55782
O═C(NCCCN1CCCC1═O)Cc1cccs1
6.556977









A number of embodiments of the disclosure have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other embodiments are within the scope of the following claims.

## Claims

1. A method of attracting one or more insect species comprising the use of a composition comprising 2-ethylpyrazine.

2. The method of claim 1, wherein said method is effected by activating odor receptors or odor receptor neurons.

3. The method of claim 1, wherein 2-ethylpyrazine is present in said composition at a concentration of from about 10% to about 99% by weight.

4. The method of claim 3, wherein 2-ethylpyrazine is present in said composition at a concentration of from about 50% to about 99% by weight.

5. The method of claim 1, wherein said composition further comprises a carrier.

6. The method of claim 1, wherein said composition further comprises a suitable solvent.

7. The method of claim 6, wherein said solvent is an organic solvent.

8. The method of claim 1, wherein said composition is present in a wicked apparatus.

9. The method of claim 2, wherein said odor receptors comprise one or more members of the Gustatory receptor (Gr) family.

10. The method of claim 9, wherein said one or more members of the Gr family comprise CO2 receptors.

11. The method of claim 10, wherein said CO2 receptors comprise Gr21a, Gr63a, AgGr22, AgGr23 and/or AgGr24 proteins.

12. The method of claim 1, wherein said insect species is selected from the group consisting of mosquitoes, sandflies and Testse flies.

13. The method of claim 12, wherein said insect species comprises mosquitoes selected from the group consisting of Anopheles gambiae, Culex pipiens and Aedes aegypti mosquitoes.

14. A method of luring one or more insect species to a trap comprising the use of a composition comprising 2-ethylpyrazine.

15. The method of claim 14, wherein said method of luring is carried out by virtue of activating odor receptors or odor receptor neurons in said one or more insect species.

16. The method of claim 14, wherein 2-ethylpyrazine is present in said composition at a concentration of from about 50% to about 99% by weight.

17. The method of claim 14, wherein said composition further comprises a carrier.

18. The method of claim 14, wherein said composition further comprises a suitable solvent.

19. The method of claim 18, wherein said solvent is an organic solvent.

20. The method of claim 14, wherein said composition is present in a wicked apparatus.

21. The method of claim 15, wherein said odor receptors comprise one or more members of the Gustatory receptor (Gr) family.

22. The method of claim 21, wherein said one or more members of the Gr family comprise CO2 receptors.

23. The method of claim 22, wherein said CO2 receptors comprise Gr21a, Gr63a, AgGr22, AgGr23 and/or AgGr24 proteins.

24. The method of claim 14, wherein said insect species is selected from the group consisting of mosquitoes, sandflies and Testse flies.

25. The method of claim 24, wherein said insect species comprises mosquitoes selected from the group consisting of Anopheles gambiae, Culex pipiens and Aedes aegypti mosquitoes.

26. The method of claim 14, wherein said trap is selected from the group consisting of a tape and a mechanical trap.

27. The method of claim 26, wherein said mechanical trap is selected from the group consisting of a suction-based trap, a light-based trap and an electric current-based trap.

28. A method of inhibiting, preventing or reducing the incidence of insect-borne disease in a subject, said method comprising luring one or more insect species to a trap comprising the use of a composition comprising 2-ethylpyrazine, thereby reducing or eliminating said one or more insect species and thereby inhibiting, preventing or reducing the incidence of insect-born disease in said subject.

29. The method of claim 28, wherein said luring is carried out by virtue of activating odor receptors or odor receptor neurons in said one or more insect species.

30. The method of claim 28, wherein 2-ethylpyrazine is present in said composition at a concentration of from about 50% to about 99% by weight.

31. The method of claim 28, wherein said composition further comprises a carrier.

32. The method of claim 28, wherein said composition further comprises a suitable solvent.

33. The method of claim 32, wherein said solvent is an organic solvent.

34. The method of claim 28, wherein said composition is present in a wicked apparatus.

35. The method of claim 28, wherein said insect-borne disease is selected from the group consisting of malaria, dengue, yellow fever, river blindness, lymphatic filariasis, sleeping sickness, leishmaniasis, epidemic polyarthritis, West Nile virus disease and Australian encephalitis.