Abstract:
Provided is a method of generating a toxicity prediction model for a microorganism, a method of predicting the toxicity of a chemical substance to a microorganism using the toxicity prediction model, and a method of assigning priorities to biosynthetic pathways for a target material using the toxicity prediction method.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of Korean Patent Application No. 10-2013-0025247, filed on Mar. 8, 2013, in the Korean Intellectual Property Office, the entire disclosure of which is incorporated herein by reference. 
       BACKGROUND 
       [0002]    1. Field 
         [0003]    The present disclosure relates to a method of predicting the toxicity of chemicals to a microorganism and a method of evaluating pathways by using their predicted toxicity. 
         [0004]    2. Description of the Related Art 
         [0005]    Metabolic engineering refers to the genetic manipulation of metabolic properties of cells or cell strains by adding a new metabolic pathway or removing, amplifying or modifying an existing metabolic pathway. Using metabolic engineering, components of a living organism may be modified to create an efficient system or a new biological system suitable for an intended goal. 
         [0006]    Toxicity is an important factor to consider in developing a metabolic pathway for the biosynthesis of metabolic products at high concentrations. A quantitative structure-activity relationship (QSAR) method is a technology that predicts a value from a quantitative correlation of the chemical structure, physicochemical properties, and toxicity of a chemical substance on the assumption that chemical substances with similar structures have similar properties. In particular, QSAR is of importance in pre-screening properties or toxicity of chemical substances under new development. 
       SUMMARY 
       [0007]    Provided is a computer-implemented method of generating a toxicity prediction model for a microorganism, a method of predicting toxicity of a chemical substance to a microorganism using the generated toxicity prediction model, and a method of assigning priorities to biosynthetic pathways for a target material using the toxicity prediction method. 
         [0008]    Additional aspects will be set forth in part in the description which follows and, in part, will be apparent from the description, or may be learned by practice of the presented embodiments. 
         [0009]    According to an aspect of the present invention, there is provided a computer-implemented method of generating a toxicity prediction model, the method including: receiving information on toxicity to a microorganism, structural properties and physicochemical properties of chemical substances; calculating molecular descriptors based on the information on structural properties and physicochemical properties; selecting molecular descriptors based on the calculated molecular descriptors and the information on toxicity; and generating a toxicity prediction model using the selected molecular descriptors to predict the toxicity of a chemical substance to the microorganism. 
         [0010]    In an exemplary embodiment of the present invention, the method may include receiving information on the toxicity to a microorganism, structural properties and physicochemical properties of chemical substances, from a database or a device that provides experimental data. The microorganism may be a prokaryote or a eukaryote. The prokaryote may be  Esherichia coli.  The eukaryote may be an yeast. The database may be a PubChem, ChemBank, DrugBank, KEGG, BRENDA, or BioCYC database. The information on toxicity may be quantitatively and/or qualitatively indicated. The quantitative information on toxicity may be an IC 50  value. IC 50  refers to a concentration of a chemical substance which inhibits the growth of a microorganism by 50%. The quantitative information on toxicity may be indicated as “toxic” or “safe.” The information on structural properties may include, for example, an inter-atomic distance between molecules of a compound, an angle between adjacent atoms, a degree of warping of molecules, molecule oscillation, and/or orbital. The information on physicochemical properties may include, for example, density, a melting point, a boiling point, a molecular weight, solubility, and/or vapor pressure. 
         [0011]    The method may include calculating molecular descriptors from the received information on structural properties and physicochemical properties. A molecular descriptor refers to a numerical value corresponding to the structure or physicochemical properties of a molecule. The calculation may be executed using a software program for calculating molecular descriptors. The molecular descriptors may include at least one selected from the group consisting of a constitutional descriptor, a physicochemical descriptor, a geometric descriptor, and an electrostatic descriptor. The molecular descriptors may further include a topological descriptor. In an exemplary embodiment of the present invention, the molecular descriptors may include a constitutional descriptor, a physicochemical descriptor, a geometric descriptor, an electrostatic descriptor, and a topological descriptor. 
         [0012]    The constitutional descriptor may include, for example, a rotatable bonds count, a molecular weight, a longest aliphatic chain, a Lipinski rule of five, a largest Pi system, a largest chain, an atom count, a bond count, an aromatic bond count, hydrogen bond acceptors, a hydrogen bond donator, an aromatic atom count, and/or atomic polarizations. The physicochemical descriptors may be numerical values which represent physico-chemical properties of substances. The physicohemical descriptor may include parameters to account for hydrophobicity, topology, electronic properties, and steric effects. The physicochemical descriptor may include, for example, X log P. The geometric descriptor may include, for example, a gravitational index, a length over breadth, a moment of inertia, and/or a Petitjean shape index. The electrostatic descriptor may include, for example, an ionizational potential, a charged partial surface area, and/or bond polarizabilities (BPol). The topological descriptor may include, for example, carbon connectivity index (order 0) (Carbon Connec Order Zero), carbon connectivity index (order 1) (Carbon Connec Order One), chi chain indices, chi cluster indices, chi path indices, chi path cluster indices, eccentric connectivity index, kappa shape indices, molecular distance edge (MDE), autocorrelation polarizability, autocorrelation charge, autocorrelation mass, petitjean number, topological polar surface area (TPSA), vertex adj magnitude, weighted path, weinner number, zagreb index, weighted holistic invariant molecular(WHIM), BOUT, atomic valence connectivity index order 0, atomic valence connectivity index order 1, and/or fragment complexity. 
         [0013]    In an exemplary embodiment of the present invention, the method may include selecting molecular descriptors based on the calculated molecular descriptors and the information on toxicity. The selection of molecular descriptors may be executed using a statistical analysis method generally used in feature selection. Feature selection refers to a process of selecting a subset of data that can improve the accuracy of classification from the original data. Feature selection may involve the extraction of features most closely related to the purpose of the classification and removing data such as redundant data and noise data which contribute less to the classification, thereby enabling a faster calculation time and more accurate classification. The statistical analysis may include, for example, principal component analysis (PCA), forward selection, backward elimination, stepwise selection, partial least-squares, and/or genetic algorithm. For example, in the case of the principal component analysis, the selection of molecular descriptors may be selection of molecular descriptors in which a cumulative proportion of importance is equal to or greater than a standard value. The proportion of importance refers to a value which represents how well a certain principle component explains information included in original variables. The sum of the proportions regarding each principle component is represented as a cumulative proportion of importance. The standard value may be selected within the range of about 50 to about 100%. 
         [0014]    The method may include generating a toxicity prediction model using the selected molecular descriptors to predict the toxicity of a chemical substance to the microorganism. The generation of a toxicity prediction model may be performed using a statistical modeling method or a pattern recognition method using artificial intelligence. The statistical modeling method or the pattern recognition method using artificial intelligence may include a statistical method such as regression analysis, or a pattern classifying method using artificial intelligence such as support vector machine (SVM) or neural network. SVM is supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on (Cortes, Corinna et al., Support-Vector Networks, Machine Learning, 20, 1995). The modeling method may include, for example, multiple linear regression, random forest regression algorithm, artificial neural network algorithm, SVM algorithm, genetic algorithm, and/or partial least-squares. 
         [0015]    In an exemplary embodiment of the present invention, the method may be executed by a processor. The processor may be part of a computing apparatus.  FIG. 2  illustrates the apparatus  10  for generating a toxicity prediction model, according to an embodiment of the present invention. Referring to  FIG. 2 , a receiving unit (receivor)  110 , a calculating unit (calculator)  120 , a selecting unit (selector)  130 , and a generating unit (generator)  140  may be included in the processor  100 . The receiving unit  110  may acquire information on the toxicity to a microorganism, structural properties, and physicochemical properties of one or more chemical substances, from a database or a device that provides experimental data. The calculating unit  120  may calculate molecular descriptors based on the received information on structural and physicochemical properties, using descriptor-calculating programs. The selecting unit  130  may select molecular descriptors useful for predicting a toxicity of a chemical substance with respect to the microorganism, based on the calculated descriptors and the received information on toxicity, using statistical analysis. The generating unit  140  may generate a toxicity prediction model with respect to the microorganism based on the selected descriptors, using modeling techniques. 
         [0016]    According to another aspect of the present invention, there is provided a method of predicting toxicity of a chemical substance to a microorganism, including: selecting a chemical substance; applying the selected chemical substance to the toxicity prediction model; and predicting the toxicity of the chemical substance to the microorganism based on the the toxicity prediction model. 
         [0017]    In an exemplary embodiment of the present invention, the method may include selecting a chemical substance, wherein the toxicity of the substance to the microorganism is to be predicted. 
         [0018]    In an exemplary embodiment of the present invention, the method may include applying the selected chemical substance to a toxicity prediction model. In an exemplary embodiment of the present invention, the toxicity prediction model may be a model generated by, for example, receiving information on toxicity to a microorganism, structural properties and physicochemical properties of chemical substances; calculating molecular descriptors based on the information on structural properties and physicochemical properties; selecting molecular descriptors based on the calculated molecular descriptors and the information on toxicity; and generating a toxicity prediction model using selected molecular descriptors. The details of each step are the same as described above. 
         [0019]    In an exemplary embodiment of the present invention, the method may include predicting toxicity of the chemical substance to the microorganism based on the toxicity prediction model. The information on toxicity may include quantitative and/or qualitative information. The quantitative information on toxicity may be IC 50  values. The qualitative information on toxicity may be indicated as “toxic” or “safe.” 
         [0020]    According to another aspect of the present invention, there is provided a method of assigning priorities to biosynthetic pathways for a target material, comprising: receiving information for a plurality of biosynthetic pathways for a target material; obtaining information on toxicity of intermediate metabolites in each biosynthetic pathway by applying the intermediate metabolites to a toxicity prediction model, and evaluating toxicity of each biosynthetic pathway; and assigning priorities to the biosynthetic pathways according to a result of the toxicity evaluation. 
         [0021]    In an exemplary embodiment of the present invention, the method may include obtaining a candidate biosynthetic pathway for a target material. The candidate biosynthetic pathway may be, for example, obtained by using a set of reaction rules. The set of reaction rules refers to a group of reaction rules which can explain one or more enzyme-substrate reactions. For example, if 100 reactions can be explained using 10 reaction rules, the 10 reaction rules may constitute a set of reaction rules regarding the 100 reactions. 
         [0022]    In an exemplary embodiment of the present invention, the method may include obtaining information on toxicity of intermediate metabolites in each biosynthetic pathway by applying the intermediate metabolites in each biosynthetic pathway to a toxicity prediction model, and evaluating toxicity of each biosynthetic pathway. The toxicity prediction model may be a model generated by, for example, a method of building a toxicity prediction model, including: receiving information on toxicity to a microorganism, structural properties and physicochemical properties of chemical substances; calculating molecular descriptors based on the information on structural properties and physicochemical properties; selecting descriptors based on the calculated molecular descriptors and the information on toxicity; and generating a toxicity prediction model using selected molecular descriptors. The details of each step are the same as described above. 
         [0023]    The toxicity values for intermediate metabolites in the biosynthetic pathway may be indicated in terms of IC 50 . The evaluation of toxicity regarding the biosynthetic pathway may involve determining the lowest IC 50  value or the average IC 50  value for the pathway. The lowest IC 50  indicates the lowest value among the predicted IC 50  values for each of the intermediate metabolites in the biosynthetic pathway. The average IC 50  indicates the value obtained by averaging the predicted IC 50  values for each of the intermediate metabolites in the biosynthetic pathway. 
         [0024]    In an exemplary embodiment of the present invention, the method may include assigning priorities to the biosynthetic pathways according to the result of the toxicity evaluation. The priority assignment may involve comparing the lowest or average IC 50  values for each pathway. For example, two candidate pathways may be considered for the biosynthesis of a target material in a microorganism. When the lowest IC 50  value or the average IC 50  value for the first pathway is higher than that for the second pathway, the toxic effects on the microorganism by the first pathway may be regarded to be lower than that by the second pathway. Thus, the first pathway may be given the priority over the second pathway. The pathway which is given the priority over other pathways may be experimentally performed to biosynthesize the target material, due to lower toxic effects on the microorganism. 
         [0025]    In assigning priorities in the biosynthetic pathways, the result of toxicity evaluation may be considered along with the reaction properties and chemical properties. The reaction properties may include, for example, thermodynamic feasibility, pathway distance and maximum theoretical yield of product. The chemical properties may include, for example, binding site covalence and chemical similarity. 
         [0026]    The methods of the present disclosure may be used, for example, to predict the toxicity of an intermediate metabolite or to re-design the biosynthetic pathway. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0027]    These and/or other aspects will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which: 
           [0028]      FIG. 1  is a diagram illustrating generating of a toxicity prediction model according to an embodiment of the present invention, and a method of predicting toxicity using the same; 
           [0029]      FIG. 2  is block diagram of the apparatus  10  for generating a toxicity prediction model, according to an embodiment of the present invention; 
           [0030]      FIG. 3  is a diagram illustrating a method of improving a success rate of biosynthesis by using a predicted toxicity in the course of creation, evaluation, and final selection of a new biosynthetic pathway; 
           [0031]      FIG. 4  is a graph illustrating the difference in the explanation power of data for molecular descriptors selected in the conventional method and those selected in a method according to an embodiment of the present invention; 
           [0032]      FIG. 5  is a diagram showing the predicted toxicity of chemical substances in the biodegradation pathway of xenobiotic compounds via a toxicity prediction model, according to an embodiment of the present invention, and 
           [0033]      FIG. 6  is a diagram showing the predicted toxicity of chemical substances in a new biosynthetic pathway of 1,4-BDO via a toxicity prediction model, according to an embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0034]    The present embodiments may have different forms and should not be construed as being limited to the descriptions set forth herein. Accordingly, the embodiments are merely described below, by referring to the figures, to explain aspects of the present description. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Expressions such as “at least one of,” when preceding a list of elements, modify the entire list of elements and do not modify the individual elements of the list. 
       EXAMPLE 1 
     Generating a Toxicity Prediction Model and Evaluation of its Accuracy 
       [0035]    In order to generate a toxicity prediction model for  E. coli,  information on 73 chemical substances with known IC 50 , as listed below, were obtained from a PubChem database. Molecular descriptors for each of the chemical substances were calculated via a chemistry development kit program using the thus obtained information (J Chem Inf Comput Sci., Steinbeck C et al., The Chemistry Development Kit (CDK): an open-source Java library for Chemo- and Bioinformatics. 2003, 43(2):493-500). A total of 178 calculated values were obtained for the 44 molecular descriptors set as the basic values in the program. If a calculated value could not be obtained for any one of the chemical substances among them, the value was eliminated. In WHIM descriptors, a total of 6 values (Wgamma1.unity, Wgamma2.unity, Wgamma3.unity, WG.unity, WD.unity, Wetal.unity) were eliminated, such that a total of 172 values were selected. The selected molecular descriptors included a constitutional descriptor, a physicochemical descriptor, a geometric descriptor, an electrostatic descriptor and a topological descriptor, and thus they could exhibit not only properties based on a partial structure but also overall properties. The molecular descriptors used in a method according to an embodiment of the present invention and their calculated values are shown in Table 1 below. 
         [0036]    73 Chemical substances used in generating a toxicity prediction model Baclofen, 2-Amino-3-methyl-1-butanol, Bornylamine, Tetrahydro-2-furoic acid, Acetylmandelic acid, 1-(4-fluorophenyl)-2-methyl-2-propylamine, 1-Phenyl-2-propyn-1-ol, 2-Bromodecanoic acid, 3-Hydroxypropionic acid, 4-(1-Pyrrolidinyl)piperidine, 4-Acetylbutyric acid, 4-Hydroxyphenylacetic acid, 4-Methylhexanoic acid, 5-Aminolevulinic acid, 5-Methoxygramine, 5-Methyl benzimidazole, 6-Bromo-1-hexanol, 7-Amino-1,3-naphthalene disulfonic acid, Ampicillin, Azithromycin, β-Alanine, Butylamine, Capreomycin, CAPSO, Cefotaxime, Cephalexin, Chloramphenicol, Congocidine, Cycloserine, Alanine, Arginine, Galacturonic acid, Glucosamic acid, Leucine, Penicillamine, Valine, 2-Aminobutyric acid, Isoleucine, Metheonine, Threo-β-hydroxyaspartic acid, Erythromycin, Fusidic acid, G418, Gentamycin, Glycine, Hygromycin B, Isoprenaline, Isovaleric acid, Kanamycin, Propargylglycine, Canavanine, Mimosine, Serine, Malic acid, Memantine, N-acetyl-alanine, N-acetyl-methionine, N-acetyl-glycine, N-methyloctylamine, Neomycin, Nicotinic acid, O-Acetyl-serine, Oleandomycin, Oxamic acid, Penicillin G, Piperacillin, Propionic acid, Pyruvic acid, Spectinomycin, Sulfacetamide, Syringaldehyde, Vanillin, and Zeocin. 
         [0000]    
       
         
               
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                   
                 Molecular 
                   
                   
               
               
                 Classification 
                 Descriptor 
                 Definition; Reference 
                 Calculated Value 
               
               
                   
               
             
             
               
                 Constitutional 
                 Rotatable Bonds 
                 Descriptor that calculates the 
                 nRotB 
               
               
                 descriptor 
                 Count 
                 number of nonrotatable bonds 
                   
               
               
                   
                   
                 on a molecule 
                   
               
               
                   
                 Molecular Weight 
                 Descriptor based on the weight 
                 MW 
               
               
                   
                   
                 of atoms of a certain element 
                   
               
               
                   
                   
                 type. If no element is specified, 
                   
               
               
                   
                   
                 the returned value is the 
                   
               
               
                   
                   
                 Molecular Weight 
                   
               
               
                   
                 Longest Aliphatic 
                 Returns the number of atoms 
                 nAtom LAC 
               
               
                   
                 Chain 
                 in the longest aliphatic chain 
                   
               
               
                   
                 Lipinski Rule of 
                 This Class contains a method 
                 LipinskiFailures 
               
               
                   
                 Five 
                 that returns the number failures 
                   
               
               
                   
                   
                 of the Lipinski&#39;s Rule Of Five. 
                   
               
               
                   
                 Largest Pi 
                 Returns the number of atoms 
                 nAtomP 
               
               
                   
                 System 
                 in the largest pi chain 
                   
               
               
                   
                 Largest Chain 
                 Returns the number of atoms 
                 nAtomLC 
               
               
                   
                   
                 in the largest chain 
                   
               
               
                   
                 Atom Count 
                 Descriptor based on the 
                 nAtom 
               
               
                   
                   
                 number of atoms of a certain 
                   
               
               
                   
                   
                 element type 
                   
               
               
                   
                 Bond Count 
                 Descriptor based on the 
                 nB 
               
               
                   
                   
                 number of bonds of a certain 
                   
               
               
                   
                   
                 bond order 
                   
               
               
                   
                 Aromatic Bond 
                 Descriptor based on the 
                 nAromBond 
               
               
                   
                 Count 
                 number of aromatic bonds of a 
                   
               
               
                   
                   
                 molecule 
                   
               
               
                   
                 Hydrogen Bond 
                 Descriptor that calculates the 
                 nHBAcc 
               
               
                   
                 Acceptors 
                 number of hydrogen bond 
                   
               
               
                   
                   
                 acceptors 
                   
               
               
                   
                 Hydrogen Bond 
                 Descriptor that calculates the 
                 nHBDon 
               
               
                   
                 Donator 
                 number of hydrogen bond 
                   
               
               
                   
                   
                 donors 
                   
               
               
                   
                 Aromatic Atom 
                 Descriptor based on the 
                 naAromAtom 
               
               
                   
                 Count 
                 number of aromatic atoms of a 
                   
               
               
                   
                   
                 molecule 
                   
               
               
                   
                 Atomic 
                 Descriptor that calculates the 
                 apol 
               
               
                   
                 Polarizations 
                 sum of the atomic 
                   
               
               
                   
                   
                 polarizabilities (including 
                   
               
               
                   
                   
                 implicit hydrogens) 
                   
               
               
                 Physicochemical 
                 XlogP 
                 Prediction of logP based on the 
                 XlogP 
               
               
                 descriptor 
                   
                 atom-type method called 
                   
               
               
                   
                   
                 XlogP; 
                   
               
               
                   
                   
                 Wang, R., Fu, Y., and Lai, L., A 
                   
               
               
                   
                   
                 New Atom-Additive Method for 
                   
               
               
                   
                   
                 Calculating Partition 
                   
               
               
                   
                   
                 Coefficients, Journal of 
                   
               
               
                   
                   
                 Chemical Information and 
                   
               
               
                   
                   
                 Computer Sciences, 1997, 
                   
               
               
                   
                   
                 37: 615-621; 
                   
               
               
                   
                   
                 Wang, R., Gao, Y., and Lai, L., 
                   
               
               
                   
                   
                 Calculating partition coefficient 
                   
               
               
                   
                   
                 by atom-additive method, 
                   
               
               
                   
                   
                 Perspectives in Drug Discovery 
                   
               
               
                   
                   
                 and Design, 2000, 19: 47-66 
                   
               
               
                 Geometric 
                 Gravitational 
                 Descriptor characterizing the 
                 GRAV-1, GRAV- 
               
               
                 descriptor 
                 Index 
                 mass distribution of the 
                 2, GRAV-3, 
               
               
                   
                   
                 molecule; 
                 GRAVH-1, 
               
               
                   
                   
                 Katritzky, A. R. and Mu, L. and 
                 GRAVH-2, 
               
               
                   
                   
                 Lobanov, V. S. and Karelson 
                 GRAVH-3, 
               
               
                   
                   
                 M., Correlation of Boiling 
                 GRAV-4, GRAV- 
               
               
                   
                   
                 Points With Molecular 
                 5, GRAV-6 
               
               
                   
                   
                 Structure. 1. A Training Set of 
                   
               
               
                   
                   
                 298 Diverse Organics and a 
                   
               
               
                   
                   
                 Test Set of 9 Simple 
                   
               
               
                   
                   
                 Inorganics, J. Phys. Chem., 
                   
               
               
                   
                   
                 1996, 100: 10400-10407; 
                   
               
               
                   
                   
                 Wessel, M. D. and Jurs, P. C. 
                   
               
               
                   
                   
                 and Tolan, J. W. and Muskal, 
                   
               
               
                   
                   
                 S. M., Prediction of Human 
                   
               
               
                   
                   
                 Intestinal Absorption of Drug 
                   
               
               
                   
                   
                 Compounds From Molecular 
                   
               
               
                   
                   
                 Structure, Journal of Chemical 
                   
               
               
                   
                   
                 Information and Computer 
                   
               
               
                   
                   
                 Sciences, 1998, 38: 726-735 
                   
               
               
                   
                 Length Over 
                 Calculates the ratio of length to 
                 LOBMAX, 
               
               
                   
                 Breadth 
                 breadth 
                 LOBMIN 
               
               
                   
                 Moment Of Inertia 
                 Descriptor that calculates the 
                 MOMI-X, MOMI- 
               
               
                   
                   
                 principal moments of inertia 
                 Y, MOMI-Z, 
               
               
                   
                   
                 and ratios of the principal 
                 MOMI-XY, MOMI- 
               
               
                   
                   
                 moments. Als calculates the 
                 XZ, MOMI-YZ, 
               
               
                   
                   
                 radius of gyration 
                 MOMI-R 
               
               
                   
                 Petitjean Shape 
                 The topological and geometric 
                 topoShape, 
               
               
                   
                 Index 
                 shape indices described 
                 geomShape 
               
               
                   
                   
                 Petitjean and Bath et al. 
                   
               
               
                   
                   
                 respectively. Both measure the 
                   
               
               
                   
                   
                 anisotropy in a molecule 
                   
               
               
                 Electrostatic 
                 Ionizational 
                 Descriptor that evaluates the 
                 IonzPot 
               
               
                 descriptor 
                 Potential 
                 ionization potential 
                   
               
               
                   
                 Charged Partial 
                 A variety of descriptors 
                 PPSA-1, PPSA-2, 
               
               
                   
                 Surface Area 
                 combining surface area and 
                 PPSA-3, PNSA-1, 
               
               
                   
                   
                 partial charge information; 
                 PNSA-2, PNSA- 
               
               
                   
                   
                 Stanton, D. T. and Jurs, P. C., 
                 3, DPSA-1, 
               
               
                   
                   
                 Development and Use of 
                 DPSA-2, DPSA- 
               
               
                   
                   
                 Charged Partial Surface Area 
                 3, FPSA-1, 
               
               
                   
                   
                 Structural Descriptors in 
                 FPSA-2, FPSA-3, 
               
               
                   
                   
                 Computer Assissted 
                 FNSA-1, FNSA-2, 
               
               
                   
                   
                 Quantitative Structure Property 
                 FNSA-3, WPSA- 
               
               
                   
                   
                 Relationship Studies, 
                 1, WPSA-2, 
               
               
                   
                   
                 Analytical Chemistry, 1990, 
                 WPSA-3, WNSA- 
               
               
                   
                   
                 62: 2323-2329 
                 1, WNSA-2, 
               
               
                   
                   
                   
                 WNSA-3, RPCG, 
               
               
                   
                   
                   
                 RNCG, RPCS, 
               
               
                   
                   
                   
                 RNCS, THSA, 
               
               
                   
                   
                   
                 TPSA, RHSA, 
               
               
                   
                   
                   
                 RPSA 
               
               
                   
                 BPol 
                 Descriptor that calculates the 
                 bpol 
               
               
                   
                   
                 sum of the absolute value of 
                   
               
               
                   
                   
                 the difference between atomic 
                   
               
               
                   
                   
                 polarizabilities of all bonded 
                   
               
               
                   
                   
                 atoms in the molecule 
                   
               
               
                   
                   
                 (including implicit hydrogens) 
                   
               
               
                 Topological 
                 Carbon Connec 
                 carbon connectivity index 
                 chi0vC 
               
               
                 descriptor 
                 Order Zero 
                 (order 0) 
                   
               
               
                   
                 Carbon Connec 
                 carbon connectivity index 
                 chi1vC 
               
               
                   
                 Order One 
                 (order 1) 
                   
               
               
                   
                 Chi Chain Indices 
                 Evaluates the Kier &amp; Hall Chi 
                 SCH-3, SCH-4, 
               
               
                   
                   
                 chain indices of orders 3, 4, 5 
                 SCH-5, SCH-6, 
               
               
                   
                   
                 and 6; 
                 SCH-7, VCH-3, 
               
               
                   
                   
                 Kier, L. B., and Hall, L. H. 
                 VCH-4, VCH-5, 
               
               
                   
                   
                 (1976). Molecular connectivity 
                 VCH-6, VCH-7 
               
               
                   
                   
                 in chemistry and drug 
                   
               
               
                   
                   
                 research, (New York: 
                   
               
               
                   
                   
                 Academic Press). 
                   
               
               
                   
                 Chi Cluster 
                 Evaluates the Kier &amp; Hall Chi 
                 SC-3, SC-4, SC- 
               
               
                   
                 Indices 
                 cluster indices of orders 3, 4, 5, 6 
                 5, SC-6, VC-3, 
               
               
                   
                   
                 and 7; 
                 VC-4, VC-5, VC-6 
               
               
                   
                   
                 Kier, L. B., and Hall, L. H. 
                   
               
               
                   
                   
                 (1976). Molecular connectivity 
                   
               
               
                   
                   
                 in chemistry and drug 
                   
               
               
                   
                   
                 research, (New York: 
                   
               
               
                   
                   
                 Academic Press). 
                   
               
               
                   
                 Chi path Indices 
                 Evaluates the Kier &amp; Hall Chi 
                 SP-0, SP-1, SP- 
               
               
                   
                   
                 path indices of orders 
                 2, SP-3, SP-4, 
               
               
                   
                   
                 0, 1, 2, 3, 4, 5, 6 and 7; 
                 SP-5, SP-6, SP- 
               
               
                   
                   
                 Kier, L. B., and Hall, L. H. 
                 7, VP-0, VP-1, 
               
               
                   
                   
                 (1976). Molecular connectivity 
                 VP-2, VP-3, VP- 
               
               
                   
                   
                 in chemistry and drug 
                 4, VP-5, VP-6, 
               
               
                   
                   
                 research, (New York: 
                 VP-7 
               
               
                   
                   
                 Academic Press). 
                   
               
               
                   
                 Chi path Cluster 
                 Evaluates the Kier &amp; Hall Chi 
                 SPC-4, SPC-5, 
               
               
                   
                 Indices 
                 path cluster indices of orders 
                 SPC-6, VPC-4, 
               
               
                   
                   
                 4, 5 and 6; 
                 VPC-5, VPC-6 
               
               
                   
                   
                 Kier, L. B., and Hall, L. H. 
                   
               
               
                   
                   
                 (1976). Molecular connectivity 
                   
               
               
                   
                   
                 in chemistry and drug 
                   
               
               
                   
                   
                 research, (New York: 
                   
               
               
                   
                   
                 Academic Press). 
                   
               
               
                   
                 Eccentric 
                 A topological descriptor 
                 ECCEN 
               
               
                   
                 Connectivity 
                 combining distance and 
                   
               
               
                   
                 Index 
                 adjacency information 
                   
               
               
                   
                 Kappa Shape Indices 
                 Descriptor that calculates Kier 
                 Kier1, Kier2, 
               
               
                   
                   
                 and Hall kappa molecular 
                 Kier3 
               
               
                   
                   
                 shape indices; 
                   
               
               
                   
                   
                 Hall, L. H., and Kier, L. B. 
                   
               
               
                   
                   
                 (1991). The molecular 
                   
               
               
                   
                   
                 connectivity chi indices and 
                   
               
               
                   
                   
                 kappa shape indices in 
                   
               
               
                   
                   
                 structure-property modeling. In 
                   
               
               
                   
                   
                 Reviews of Computational 
                   
               
               
                   
                   
                 Chemistry, K. B. Lipkowitz, and 
                   
               
               
                   
                   
                 D. B. Boyd, eds. (New York: 
                   
               
               
                   
                   
                 VCH publishers), pp. 367-412. 
                   
               
               
                   
                 MDE 
                 Evaluate molecular distance 
                 MDEC-11, 
               
               
                   
                   
                 edge descriptors for C, N and 
                 MDEC-12, 
               
               
                   
                   
                 O; 
                 MDEC-13, 
               
               
                   
                   
                 Liu, S. and Cao, C. and Li, Z. , 
                 MDEC-14, 
               
               
                   
                   
                 Approach to Estimation and 
                 MDEC-22, 
               
               
                   
                   
                 Prediction for Normal Boiling 
                 MDEC-23, 
               
               
                   
                   
                 Point (NBP) of Alkanes Based 
                 MDEC-24, 
               
               
                   
                   
                 on a Novel Molecular Distance 
                 MDEC-33, 
               
               
                   
                   
                 Edge (MDE) Vector, lambda, 
                 MDEC-34, 
               
               
                   
                   
                 Journal of Chemical 
                 MDEC-44, 
               
               
                   
                   
                 Information and Computer 
                 MDEO-11, 
               
               
                   
                   
                 Sciences, 1998, 38: 387-394 
                 MDEO-12, 
               
               
                   
                   
                   
                 MDEO-22, 
               
               
                   
                   
                   
                 MDEN-11, 
               
               
                   
                   
                   
                 MDEN-12, 
               
               
                   
                   
                   
                 MDEN-13, 
               
               
                   
                   
                   
                 MDEN-22, 
               
               
                   
                   
                   
                 MDEN-23, 
               
               
                   
                   
                   
                 MDEN-33 
               
               
                   
                 Autocorrelation 
                 Moreau-Broto autocorrelation 
                 ATSp1, ATSp2, 
               
               
                   
                 Polarizability 
                 descriptors using polarizability; 
                 ATSp3, ATSp4, 
               
               
                   
                   
                 Moreau G. and Broto P., The 
                 ATSp5 
               
               
                   
                   
                 autocorrelation of a topological 
                   
               
               
                   
                   
                 structure: A new molecular 
                   
               
               
                   
                   
                 descriptor, Nouveau Journal de 
                   
               
               
                   
                   
                 Chimie, 1980, ?: 359-360 
                   
               
               
                   
                 Autocorrelation 
                 Moreau-Broto autocorrelation 
                 ATSc1, ATSc2, 
               
               
                   
                 Charge 
                 descriptors using partial 
                 ATSc3, ATSc4, 
               
               
                   
                   
                 charges; 
                 ATSc5 
               
               
                   
                   
                 Moreau G. and Broto P., The 
                   
               
               
                   
                   
                 autocorrelation of a topological 
                   
               
               
                   
                   
                 structure: A new molecular 
                   
               
               
                   
                   
                 descriptor, Nouveau Journal de 
                   
               
               
                   
                   
                 Chimie, 1980, ?: 359-360 
                   
               
               
                   
                 Autocorrelation 
                 Moreau-Broto autocorrelation 
                 ATSm1, ATSm2, 
               
               
                   
                 Mass 
                 descriptors using atomic 
                 ATSm3, ATSm4, 
               
               
                   
                   
                 weight; 
                 ATSm5 
               
               
                   
                   
                 Moreau G. and Broto P., The 
                   
               
               
                   
                   
                 autocorrelation of a topological 
                   
               
               
                   
                   
                 structure: A new molecular 
                   
               
               
                   
                   
                 descriptor, Nouveau Journal de 
                   
               
               
                   
                   
                 Chimie, 1980, ?: 359-360 
                   
               
               
                   
                 Petitjean Number 
                 Descriptor that calculates the 
                 PetitjeanNumber 
               
               
                   
                   
                 Petitjean Number of a 
                   
               
               
                   
                   
                 molecule 
                   
               
               
                   
                 TPSA 
                 Calculation of topological polar 
                 TopoPSA 
               
               
                   
                   
                 surface area based on 
                   
               
               
                   
                   
                 fragment contributions; 
                   
               
               
                   
                   
                 Ertl, P. and Rohde, B. and 
                   
               
               
                   
                   
                 Selzer, P., Fast Calculation of 
                   
               
               
                   
                   
                 Molecular Polar Surface Area 
                   
               
               
                   
                   
                 as a Sum of Fragment-Based 
                   
               
               
                   
                   
                 Contributions and Its 
                   
               
               
                   
                   
                 Application to the Prediction of 
                   
               
               
                   
                   
                 Drug Transport Properties, J. 
                   
               
               
                   
                   
                 Med. Chem., 2000, 43: 3714- 
                   
               
               
                   
                   
                 3717 
                   
               
               
                   
                 Vertex Adj 
                 Descriptor that calculates the 
                 VAdjMat 
               
               
                   
                 Magnitude 
                 vertex adjacency information of 
                   
               
               
                   
                   
                 a molecule 
                   
               
               
                   
                 Weighted Path 
                 The weighted path (molecular 
                 WTPT-1, WTPT- 
               
               
                   
                   
                 ID) descriptors described by 
                 2, WTPT-3, 
               
               
                   
                   
                 Randic. They characterize 
                 WTPT-4, WTPT-5 
               
               
                   
                   
                 molecular branching; 
                   
               
               
                   
                   
                 Randic, M., On molecular 
                   
               
               
                   
                   
                 identification numbers, Journal 
                   
               
               
                   
                   
                 of Chemical Information and 
                   
               
               
                   
                   
                 Computer Science, 1984, 
                   
               
               
                   
                   
                 24: 164-175 
                   
               
               
                   
                 Weinner Number 
                 This class calculates Wiener 
                 WPATH, WPOL 
               
               
                   
                   
                 path number and Wiener 
                   
               
               
                   
                   
                 polarity number; 
                   
               
               
                   
                   
                 Wiener, Harry, Structural 
                   
               
               
                   
                   
                 Determination of Paraffin 
                   
               
               
                   
                   
                 Boiling Points, Journal of the 
                   
               
               
                   
                   
                 American Chemical Society, 
                   
               
               
                   
                   
                 1947, 69: 17-20 
                   
               
               
                   
                 Zagreb Index 
                 The sum of the squared atom 
                 Zagreb 
               
               
                   
                   
                 degrees of all heavy atoms 
                   
               
               
                   
                 WHIM 
                 Holistic descriptors described 
                 Wlambda1.unity, 
               
               
                   
                   
                 by Todeschini et al; 
                 Wlambda2.unity, 
               
               
                   
                   
                 Todeschini, R. and Gramatica, 
                 Wlambda3.unity, 
               
               
                   
                   
                 P., New 3D Molecular 
                 Wnu1.unity, 
               
               
                   
                   
                 Descriptors: The WHIM theory 
                 Wnu2.unity, 
               
               
                   
                   
                 and QAR Applications, 
                 Wgamma1.unity, 
               
               
                   
                   
                 Persepectives in Drug 
                 Wgamma2.unity, 
               
               
                   
                   
                 Discovery and Design, 
                 Wgamma3.unity, 
               
               
                   
                   
                 1998, ?: 355-380 
                 Weta1.unity, 
               
               
                   
                   
                   
                 Weta2.unity, 
               
               
                   
                   
                   
                 Weta3.unity, 
               
               
                   
                   
                   
                 WT.unity, 
               
               
                   
                   
                   
                 WA.unity, 
               
               
                   
                   
                   
                 WV.unity, 
               
               
                   
                   
                   
                 WK.unity, 
               
               
                   
                   
                   
                 WG.unity, 
               
               
                   
                   
                   
                 WD.unity 
               
               
                   
                 BCUT 
                 Eigenvalue based descriptor 
                 BCUTw-1l, 
               
               
                   
                   
                 noted for its utility in chemical 
                 BCUTw-1h, 
               
               
                   
                   
                 diversity described by 
                 BCUTc-1l, 
               
               
                   
                   
                 Pearlman et al.; 
                 BCUTc-1h, 
               
               
                   
                   
                 Pearlman, R. S. and Smith, 
                 BCUTp-1l, 
               
               
                   
                   
                 K. M., Metric Validation and the 
                 BCUTp-1h 
               
               
                   
                   
                 Receptor-Relevant Subspace 
                   
               
               
                   
                   
                 Concept, J. Chem. Inf. 
                   
               
               
                   
                   
                 Comput. Sci., 1999, 39: 28-35; 
                   
               
               
                   
                   
                 Burden, F. R., Molecular 
                   
               
               
                   
                   
                 identification number for 
                   
               
               
                   
                   
                 substructure searches, J. 
                   
               
               
                   
                   
                 Chem. Inf. Comput. Sci., 1989, 
                   
               
               
                   
                   
                 29: 225-227; 
                   
               
               
                   
                   
                 Burden, F. R., Chemically 
                   
               
               
                   
                   
                 Intuitive Molecular Index, 
                   
               
               
                   
                   
                 Quant. Struct.-Act. Relat., 
                   
               
               
                   
                   
                 1997, 16: 309-314; 
                   
               
               
                   
                   
                 Kang, Y. K. and Jhon, M. S., 
                   
               
               
                   
                   
                 Additivity of Atomic Static 
                   
               
               
                   
                   
                 Polarizabilities and Dispersion 
                   
               
               
                   
                   
                 Coefficients, Theoretica 
                   
               
               
                   
                   
                 Chimica Acta, 1982, 61: 41-48 
                   
               
               
                   
                 Atomic valence 
                 The sum of 1/sqrt(vi) over all 
                 chi0v 
               
               
                   
                 connectivity index 
                 heavy atoms i with vi &gt; 0 
                   
               
               
                   
                 (order 0) 
                   
                   
               
               
                   
                 Atomic valence 
                 The sum of 1/sqrt(vivj) over all 
                 chi1v 
               
               
                   
                 connectivity index 
                 bonds between heavy atoms i 
                   
               
               
                   
                 (order1) 
                 and j where i &lt; j 
                   
               
               
                   
                 Fragment 
                 Class that returns the 
                 fragC 
               
               
                   
                 Complexity 
                 complexity of a system. The 
                   
               
               
                   
                   
                 complexity is defined as 
                   
               
               
                   
                   
                 @cdk.cite{Nilakantan06}; 
                   
               
               
                   
                   
                 Nilakantan, R. and Nunn, D. S. 
                   
               
               
                   
                   
                 and Greenblatt, L. and Walker, 
                   
               
               
                   
                   
                 G. and Haraki, K. and Mobilio, 
                   
               
               
                   
                   
                 D., A family of ring system- 
                   
               
               
                   
                   
                 based structural fragments for 
                   
               
               
                   
                   
                 use in structure-activity 
                   
               
               
                   
                   
                 studies: database mining and 
                   
               
               
                   
                   
                 recursive partitioning., Journal 
                   
               
               
                   
                   
                 of chemical information and 
                   
               
               
                   
                   
                 modeling, 2006, 46: 1069-1077 
               
               
                   
               
             
          
         
       
     
         [0037]    Among the 172 calculated values, two principal components were selected based on a cumulative proportion of importance of 65% or higher after performing principal component analysis (PCA), and then, a toxicity prediction model was generated via a support vector machine (SVM), which is an artificial intelligence method (method 1). Meanwhile, 254 calculated values were obtained using only a topological descriptor for the 73 chemical substances, referring to the conventional Faulon&#39;s method (Biotechnology and Bioengineering, Vol. 109, No. 3, March, 2012). Twenty principal components were selected by performing PCA analysis based on a cumulative proportion of importance of 65% or higher, and a toxicity prediction model was generated via the SVM method for comparison with method 1 (method 2). R 2  values between the predicted value and the real value, regarding the IC 50  value predicted in the toxicity prediction model generated according to the method 1 and the IC 50  value according to method 2, were compared using a leave-one-out method. Leave-one-out involves using a single observation from the original sample as the validation data, and the remaining observations as the training data. This is repeated such that each observation in the sample is used once as the validation data. As shown in Table 2 below, the R 2  value of the method 1 was shown to be greater than that of the method 2 thus confirming that the prediction model of the present invention is more accurate than the prediction model of the conventional method. 
         [0000]    
       
         
               
               
               
               
             
           
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 Method 
                 Method 1 
                 Method 2 
               
               
                   
                   
               
             
             
               
                   
                 R 2  between predicted 
                 0.605 
                 0.525 
               
               
                   
                 value and real value 
                   
                   
               
               
                   
                   
               
             
          
         
       
     
         [0038]    Furthermore, as shown in  FIG. 4 , the number of principal components necessary for the explanation of 65% of data in the method 2 was twenty while in the method 1 only two principal components were able to explain 65% of data. From this, it was confirmed that when a prediction model was generated using a molecular descriptor along with a topological molecular descriptor as in the present invention, as compared with a conventional prediction model generated based on a topological molecular descriptor alone, data explanation was possible with a fewer number of principal components. 
       EXAMPLE 2 
     Prediction of Toxicity of Chemical Substances within TCA Cycle 
       [0039]    IC 50  values of intermediate metabolites in a TCA cycle were obtained by applying them in a toxicity prediction model. As shown in Table 3, the toxicity of the materials was not of significance. From this, it was confirmed that the prediction model may be useful in the prediction of toxicity. 
         [0000]    
       
         
               
               
               
             
               
               
               
             
           
               
                   
                 TABLE 3 
               
               
                   
                   
               
               
                   
                 Intermediate Metabolites in TCA 
                 IC 50   
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 Glucose 
                 4.212 
               
               
                   
                 Citrate 
                 4.006 
               
               
                   
                 Aconitate 
                 5.072 
               
               
                   
                 Isocitrate 
                 2.944 
               
               
                   
                 α-ketoglutarate 
                 9.222 
               
               
                   
                 Succinate 
                 11.43 
               
               
                   
                 Fumarate 
                 10.33 
               
               
                   
                 Malate 
                 9.176 
               
               
                   
                 Oxaloacetate 
                 6.327 
               
               
                   
                   
               
             
          
         
       
     
       EXAMPLE 3 
     Prediction of Toxicity of Antibiotics and Natural Metabolites 
       [0040]    IC 50  values of antibiotics and natural metabolites were obtained by applying them to a toxicity prediction model. As shown in Table 4, antibiotics were shown to have a considerable toxicity to microorganisms whereas natural metabolites were shown to have a relatively weak toxicity. From this, it was confirmed that the prediction model may be useful in the prediction of toxicity. 
         [0000]    
       
         
               
               
               
               
             
               
               
               
               
             
           
               
                   
                 TABLE 4 
               
               
                   
                   
               
               
                   
                   
                 Chemical Substances 
                 IC 50   
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 Antibiotics 
                 Ampicillin 
                 0.009 
               
               
                   
                   
                 Chloramphenicol 
                 0.412 
               
               
                   
                   
                 Erythromycin 
                 0.069 
               
               
                   
                   
                 Gentamycin 
                 0.002 
               
               
                   
                   
                 Penicillin 
                 0.047 
               
               
                   
                   
                 Streptomycin 
                 0.026 
               
               
                   
                   
                 Sulfisoxazol 
                 0.701 
               
               
                   
                   
                 Tobramycin 
                 0.003 
               
               
                   
                 Natural 
                 Fructose 
                 5.680 
               
               
                   
                 Metabolites 
                 Glucose 
                 4.212 
               
               
                   
                   
                 Glycerol 
                 6.521 
               
               
                   
                   
                 Xylose 
                 9.244 
               
               
                   
                   
                 Galactose 
                 4.212 
               
               
                   
                   
                 Fumaric acid 
                 10.330 
               
               
                   
                   
                 homoserine 
                 11.328 
               
               
                   
                   
                 1-threonine 
                 10.992 
               
               
                   
                   
                 Malic acid 
                 9.176 
               
               
                   
                   
                 Oxaloacetate 
                 6.327 
               
               
                   
                   
                 Succinic acid 
                 11.429 
               
               
                   
                   
               
             
          
         
       
     
       EXAMPLE 4 
     Prediction of Toxicity of Chemical Substances in the Biodegradation Pathway of Xenobiotic Compounds 
       [0041]    The toxicities of chemical substances in a biodegradation pathway of xenobiotic compounds, suggested in reference (Biotechnology Journal, 2010, 5(7):739-50), were predicted.  FIG. 5  shows a result of predicted toxicity of chemical substances in the biodegradation pathway of xenobiotic compounds via a toxicity prediction model according to an embodiment of the present invention. The values within parentheses represent predicted IC 50  values. In each biodegradation pathway, the toxicity of compounds was gradually decreased as the biodegradation proceeded. 
       EXAMPLE 5 
     Prediction of Toxicity of Chemical Substances in a New Biosynthetic Pathway for 1,4-Butanediol 
       [0042]    A suggested new biosynthetic pathway for 1,4-Butanediol (1,4-BDO) was re-evaluated using the toxicity values predicted via the toxicity prediction model. In the reference Nature Chemical Biology, 2011, 7(7): 445-52, the biosynthetic pathways were selected considering pathway distance, reactivity, theoretical yield of product, and chemical properties of intermediate metabolites. Of the pathways, only one pathway was found to be successful for the synthesis of 1,4-BDO. 
         [0043]      FIG. 6  shows a result of predicted toxicity of chemical substances in a new biosynthetic pathway of 1,4-BDO via a toxicity prediction model according to an embodiment of the present invention. The suggested new biosynthetic pathway was re-evaluated using the toxicity values obtained via the toxicity prediction model along with consideration of pathway distance, reactivity, theoretical yield of product, and chemical properties of intermediate metabolites. Priorities were given to the intermediate metabolites so that the higher the lowest IC 50  of an intermediate metabolite the better the priorities. As shown in  FIG. 6 , except the overlapping 4-hydroxybutanal and 1,4-BDO in the pathway, the lowest IC 50  values predicted were 14.81, 1.234, 0.447, and 0.448, respectively, thus the first pathway was ranked first. From this, it was confirmed that the success rate of biosynthesis can be increased by evaluating a new biosynthetic pathway using the toxicity prediction method along with the conventional four factors, which are pathway distance, reactivity, theoretical yield of product, and chemical properties of intermediate metabolites. 
         [0044]    It should be understood that the exemplary embodiments described therein should be considered in a descriptive sense only and not for purposes of limitation. Descriptions of features or aspects within each embodiment should typically be considered as available for other similar features or aspects in other embodiments.