dataesr/scientific-paragraphs-categorization huggingface dataset:
The samples were scanned from 10 to 70°t wo-theta with a step size of 0. 05°and a dwell time of 3 s per step. Jade software was used to analyze the data and the Scherrer equation was used to estimate the crystallite size. Hydrogen chemisorption. Hydrogen chemisorption adsorption isotherm studies were conducted using a Micromeritics ASAP 2020C instrument. In total 50 mg of the sample was loaded in a chemisorption tube and packed using quartz wool. The sample was reduced in-situ at 350 °C followed by long evacuation steps to remove the adsorbed hydrogen. Hydrogen was then re-introduced in the system at increasing pressures (2-450 mm Hg) under isothermal conditions (35 °C) for studying the first adsorption isotherm and the amount of total adsorbed hydrogen (H T ) was calculated. A long evacuation step was then applied to remove the weakly or reversibly bound hydrogen and then a second adsorption isotherm was recorded to estimate the reversibly adsorbed hydrogen (H R ).
The size of the Pd nanoparticles was obtained using H T and the ratio of the H R /H I was calculated to compare the catalysts. X-ray photoelectron spectroscopy. Lab source XPS studies were conducted using a Kratos Amicus ESCA 3400 instrument. The anode used for the analysis was Mg (240 W) with the photon energy of K alpha 1 peak at 1253. 688 eV and K alpha 2 peak at 1253. 437 eV. The samples were loaded on an indium foil used here as an internal standard. A dwell time of 1 s, step size of 50 meV, and a pass energy of 150 eV were used. Five sweeps were recorded for each sample. Sputtered In foil and HOPG samples were used as standards for data analysis for Pd and C. Data analysis was conducted using the Fitt-win software. Synchrotron radiation studies were conducted during single bunch mode at the ISISS beamline of the Fritz Haber Institute located at the BESSY II synchrotron facility in Berlin.
Density functional theory calculations. Plane-wave density functional theory calculations were performed using the Vienna ab initio simulation package (VASP)  65  to calculate interaction energies between Pd particles and C-supports (with and without functionalization) and binding energies of probe adsorbates (H * , O * , C * , OH * , and CH * ). Plane-waves were constructed using an energy cutoff of 400 eV with projector augmented wave potentials  66. The revised Perdew-Burke-Ernzerhof (RPBE) form of the generalized gradient approximation (GGA) was used to determine exchange and correlation energies  67, 68. Wavefunctions were converged to within 10 -6 eV and forces were computed using a fast Fourier transform grid with a cutoff of twice the planewave cutoff. A 1 × 1 × 1 Γpoint sampling of the first Brillouin zone (k-point mesh) was used and structures were relaxed until forces on unconstrained atoms were <0.
ACKNOWLEDGMENTS We acknowledge support from the  CONICET  under Project Nos. PUE 22920160100054CO  and  PIP 0414 , the  ANPCyT  under Project No. PICT 2013-2098 , the  Universidad Nacional del Litoral  under project  CAI  þ  D PI 84-099 , and the  MINCyT -Ecos Sud  under Project No. A13E02.
Viscosity The viscosity of an electrolyte is an importantp arameterw hich contributest ot he transport capability of the active substances during electrochemical processes. To study the effect of the cation structure on this property,t he viscosity of the ILs was measured betweent he temperature range of ca. 293-363 K. The temperature dependences of the viscosity are presented in the form of Arrhenius-type plots in Figure  5. The numerical raw viscosity data is presented in Ta ble S3. These plots show that the majority of the ILs do not follow Arrhenius-type behaviour. A si sc ommonf or ILs, the temperature dependence of viscosity, h,c an be well described using the Vogel-Tammann-Fulcher(VTF) Equation (  3 ): h ¼ h o exp B h T À T h o ð3Þ where h o , B h and T o h are the fitting parameters and T is the temperature.
The activation energy (i. e. E a = R•gradient) was then taken as an average of the calculated values within the stated temperature range. The coefficients of the polynomial expansions and the derived average E a values are shown in Ta bles S6 and S7. Interestingly, the ratio between the two activation energies, E a L /E a h (detailed in Table  6 ) closely represent the derived values for the exponent of the fractional Walden rule, a W. I nf act, the majority of the values for the studied ILs differ by less than 2% (bracketed values in Ta ble 6). This is in agreement with the observations, and the originalh ypothesis of this relation,d etailed by Schreiner et al. [61]  As mentioned previously,t he T o values derived from VTF correlationo fv iscosity and conductivity data are related to the IL'sglass transition temperature, T g (as measuredbyDSC).
(  3 ) and(  4 );a dditional notation of as uperscript f is included here to differentiate between parameters used in these and the previous equations]. F or each dataset, an additional point of h(T g ) = 10 15 mPa so rs(T g ) = 10 À12 mS cm À1 was included in the correlation leading to slightly differentc orrelation parameters. These values represent an approximation of the sample viscosity andc onductivity,r espectively,a tt he T g of the material. [61, 63]   he correlation parameters of these fits, detailed in Ta ble S8,a re then used to extrapolate the temperature dependant behaviour of viscosity/conductivity towards the T g. This is presented in the form of an Angell-type plot (Figure  9 ), where the temperature along the x-axis is scaled by the material's T g. T he fragilityi ndex, m,c alculated using Equation (11)   Table  6.
For viscosity,t his represents am inimum fragility of 16 determined by the logarithmic ratio of the viscosity at T g and an approximation of the limiting viscosity (m min = Log 10 [h(T g )/h o f ] = 16 where h o f % 0. 1 mPa s). This trend is supported by the behaviour of strong glass formers, for example, SiO 2 and GeO 2. [58] For the conductivity plot,s ince the limiting conductivity is generally considerably more varied, the y-axis intercepto ft he hypothetical Arrhenius line on the Angellp lots was approximatedf rom the average s o f correlation parameter for the studied ILs [i. e. s o f = 2. 86 mS cm À1 ,E q. (  10 )]. T his yieldeda na pproximate value of 15 for the fragility index of the ideal Arrhenius line for conductivity. The data presented in Figure  9s  hows the studied ILs all exhibit very similar, high fragility behaviour.
Acknowledgements The authors would like to gratefully acknowledget he funding from  EPSRC  ( EP/L505262/1 ) and  Innovate UK  for the Practical Lithium-AirB atteriesp roject (project number:  101577 ). Additionally,t he authors would like thank  Keith Whiston  and  INVISTAI ntermediates  for supplying the samples of azepane for this work.
Acknowledgements A. S. would like to thank for the nancial support received for dissertation preparation under  ETIUDA II program , which is nanced by the  Polish National Science Centre  (doctoral scholarship no. Dec-2014/12/T/ST4/00233 ). The work of  C. A. Lazar  was funded by the  Sectoral Operational Programme Human Resources Development 2007-2013  of the Ministry of European Funds through the  Financial Agreement   POSDRU/ 159/1. 5/S/132397. B. K. acknowledges the  Pays de la Loire region  for the nancial support of research works in the framework of the " Molecular Systems for Nonlinear Optical Application" (MOSNOA) LUMOMAT  project.
Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. Despite progress, target prediction methods still have important limitations. For example, the most accurate methods implicitly restrict their predictions to a relatively small number of targets, are not systematically validated on drugs (whose targets are harder to predict than those of non-drug molecules) and often lack a reliability score associated with each predicted target. Here we present a systematic validation of ligand-centric target prediction methods on a set of clinical drugs. These methods exploit a knowledge-base covering 887,435 known ligand-target associations between 504,755 molecules and 4,167 targets. Based on this dataset, we provide a new estimate of the polypharmacology of drugs, which on average have 11.
This nomenclature is different from that employed by target-centric methods, where query and database molecules are generally referred to as test and training sets, respectively. There are fewer methods in the ligand-centric category and these are based on molecular similarity  40  (e. g. , ChemMapper  41  , ElectroShape Polypharmacology server)  42  or on the similarity of bioactivity spectra (e. g. COMPARE)  18. It is worth noting that not all methods employing molecular similarity are ligand-centric. This is the case of TAMOSIC  32  , which learns the optimal similarity cut-off for each target with at least 30 cognate ligands, and SEA  34  , which only builds a statistical model for a target if it is characterised by at least five samples (ligands). As discussed in a previous study  7  , we are interested in ligand-centric target prediction methods because they provide the maximum coverage of the target space for a given data set.
Results and Discussion Experimental design. Figure  1  illustrates how the molecular targets of a query molecule are predicted with a ligand-centric target prediction method and how the performance of such a prediction is measured. In a nutshell, once the chemical structure fingerprint and associated similarity score is selected (see the Methods section), the similarity scores between the query molecule and each of the database molecules are calculated. Similarity scores are employed to identify the top k molecules closest to the query molecule (an approved drug out of the 745 considered in this study). Next, target annotations are extracted from the ChEMBL database  43  in order to determine the known targets for the query as well as those for the top k hits. The method uses the known targets for the top k hit molecules to predict the targets for the query molecule as well as estimate the reliability of each predicted target.
Lastly, the known targets of the query molecule permit measuring the predictive performance of the target prediction method in this binary classification problem. As usual, predicted targets for the query molecule will be divided into the four categories of the confusion matrix: TP is the number of true positive predictions (the predicted target is a known target of the query molecule), TN is the number of true negatives (the target was not predicted and is not known to be a target), FP is the number of false positives (the predicted target is not known to be a target, i. e. a false discovery or Type I error) and Figure  1. Ligand-centric target prediction workflow. The selected molecular similarity method is used to find the top k most similar database molecules to the query molecule (vorinostat in this illustrative example).
Known targets for the query and the top k hit molecules are retrieved from the ChEMBL database. A novel method is introduced to assign a reliability score for each query-target association prediction based on the proportion of the query's top hits binding to the predicted target. Lastly, the known targets of the query molecule permit measuring the predictive performance of the method at each reliability level in this binary classification problem. FN is the number of false negatives (the target was not predicted and it is actually a target, i. e. , missed discovery or Type II error). From these quantities, we will calculate five performance metrics per query molecule. Accuracy (ACC) is the proportion of correct predictions. Precision (PR) is the proportion of correctly predicted targets, i.
These data inclusion criteria lead to 887,435 known ligand-target associations in the knowledge-base, with 504,755 database molecules to screen annotated with 4,167 targets verifying the above requirements. This represents a substantial enhancement with respect to the knowledge-base used in our previous study 7 : 216% more ligand-target associations (from 281,270 to 887,435), 40% more targets (from 2,982 to 4,167) and 175% more database molecules (from 183,293 to 504,755). When applied to the set of query molecules (the 745 approved drugs), we passed from the 8. 3 single-protein targets that were on average known for an approved drug  7  to the new estimation of 11. 5 molecular targets when adding interactions with homologous proteins and protein complexes as well as more bioactivity data as explained above.
An early estimate of the degree of drug polypharmacology is an average of 6. 3 targets per drug based on data containing 5,215 drug-target associations and 557 targets  45. Owing to using a more complete data set comprising 8,535 drug-target associations and 1,427 targets, we have now a more accurate estimation of the average number of targets hitting a drug, which almost doubles the best possible estimate nine years ago. Taken together, the results indicate that the true degree of drug polypharmacology is even higher than 11. 5 targets per drug, as using more data should reveal in the future. Lastly, while this study exploits the ChEMBL database to build and validate ligand-centric target prediction methods as well as their coupled reliability prediction method, any other database with target-annotated molecules of known chemical structure can also be used with this purpose (e.
How target prediction performance varies with a new knowledge-base. We have previously evaluated the performance of a simple target prediction method, MACCS fingerprints using dice score with k = 10 and a smaller knowledge-base, on a test set with 745 approved drugs  7. Table  1  shows the corresponding results using the expanded knowledge-base allowing us to improve predictive performance. It is not trivial to anticipate how more data will affect the measured performance of the method because, among other unavoidable biases, drugs are more intensively studied than non-drug molecules  7. However, the true performance of the method should improve as the target annotation of the database molecules becomes more comprehensive. The results of expanding the knowledge-base are shown in Table  1. We can see that using the expanded knowledge-base translates to a substantial increase in the number of predicted targets (11.
From now on, we exclusively use the expanded knowledge-base, as this provides the largest number of predicted targets (i. e. avNPT values). We started by looking for the optimal value for the k parameter (the number of top hits whose database annotation is used to predict targets). We considered k = 5,10,20,25 because at k = 25 the avMCC of the method has strongly declined (data no shown). As avNPT increases with growing k, we selected the lowest k for which avNPT is greater than avNKT (the average number of known targets across query molecules). If k was smaller than 10, avNPT < avNKT and thus the method would generally predict fewer targets that are known for the target. If k was higher, the performance would tend to be worse as target inferences would be drawn from increasingly less similar database molecules.
We also considered the alternative approach of using a similarity cutoff to determine the most similar database molecules with which to form the set of predicted targets for the query molecule (instead of using the top 10 most similar molecules). Table  3  shows the results for the best method in Table 2 (Morgan_hashed_bv_2_2048) using similarity cutoffs 90%, 80%, 70%, 60% and 50%. nNullQueries is the number of query molecules for which no hits are found (i. e. no database molecule obtains a similarity score higher than the employed cutoff and thus targets cannot be predicted for these drugs). In contrast, nQueries is the number of query molecules for which at least a hit is found (thus, performance is now averaged over nQueries). From these results, we can see that a 60% cutoff provides the best performance (avMCC = 0.
nNullQueries is the number of query molecules for which no hits are found. In contrast, nQueries is the number of query molecules for which at least a hit is found (thus performance is now averaged over nQueries). AvNHITS is the average number of database molecules with similarity scores above the cutoff. The 60% cutoff provides the best performance (avMCC = 0. 338 leaving 69 drugs without predicted targets), which is slightly worse than that from using the top 10 most similar this (avMCC = 0. 339 leaving no drugs without predicted targets). hit rate of experimental validations by only testing the most reliable predictions. For instance, Fig. 2  shows that a target predicted with L = 0. 4, i. e. 4 out of the 10 most similar molecules are annotated with this target, will be on average a true target of the query molecule 50% of the times.
In particular, the number of known ligands for a target does not have any influence on which targets are predicted. Case studies. To better illustrate the strengths and limitations of this new method, we focus on the target predictions that are predicted to be most reliable (L = 1). From Table  5 , we see that there are 81 targets predictions predicted with maximum reliability, of which 7 are flagged as possible false positives and 74 were confirmed as true positives. These seven false-positive target predictions were made for five query drugs. The first of these drugs is Solifenacin succinate (CHEMBL1200803), which is the succinate salt of its first molecular hit (CHEMBL606901) and therefore both compounds should have the same targets. Three targets are annotated in all its hits, which are Rattus norvegicus Muscarinic acetylcholine receptors M1 (CHEMBL276), M2 (CHEMBL309) and M3 (CHEMBL320).
On the other hand, the targets of this drug are Homo sapiens Muscarinic acetylcholine receptors M1 (CHEMBL216), M2 (CHEMBL211) and M3 (CHEMBL245). Thus, it is very likely that the hits will also bind Boxplot showing how the precision of predicted targets varies depending on L (NB: l1 is l = 1 and corresponds to L = 0. 1). The mean precision for a given L is marked with a blue line, whereas the median is given by a red line. Table  5. Performance results for the best method (quantification of results from Fig. 2 ). The mean and median values for precision (PR) are shown, as well as the number of query molecules with a given l value. the highly similar human homologues of these receptors (sequence identity ranging from 91. 7% to 98. 7%). In that case, these three predicted targets would become true targets of Solifenacin succinate.
Second, Neostigmine (CHEMBL278020) and its bromine salt (CHEMBL54126) are the query molecule and its first hit, respectively (again both compounds contain the same molecule and hence should have the same targets). Its top hits, but not Neostigmine, are all annotated to bind Homo sapiens acetylcholinesterase (CHEMBL220). Hence this is apparently a false-positive target prediction. However, Torpedo californica acetylcholinesterase (CHEMBL4780) is annotated as a target of Neostigmine and the mechanism of action of this clinically approved drug according to the CHEMBL database is "Acetylcholinesterase inhibitor". Therefore, Neostigmine should also bind to the predicted target. The next query drugs are Citalopram (CHEMBL1200781) and Escitalopram (CHEMBL1200322), which are the racemic form and its S enantiomer respectively.
All the top hits of each of these drugs bind Rattus norvegicus Serotonin transporter (CHEMBL313). Visual inspection of both query molecules and their top hits revealed the very high degree of similarity between these, which indicates that the predicted target is very likely to be a true target of these two drugs. The latter is further supported by the binding of both drugs to human Serotonin transporter (CHEMBL228), with the S enantiomer being more potent than the racemic mixture. Taking all this into account, it is reasonable to think that these six apparent false positives will be revealed as true targets of their respective drugs once tested. Next, we study the last of these L = 1 false positives in more detail. This is the prediction of Homo sapiens Carbonic anhydrase II (CHEMBL205) as a target of Busulfan (CHEMBL820).
While the top 10 hits of Busulfan bind this enzyme (Fig. 3 ), Busulfan itself is not annotated as one of the ligands of this target. Busulfan is an alkylating agent used in cancer therapy that forms DNA-DNA intra-strand cross-links between the DNA bases guanine and adenine and between guanine and guanine through a SN2 reaction with mesylate groups -OSO 2 CH 3 as leaving groups  49. Mesylate groups are not present in any of the top hits, which are much more similar among them than with Busulfan. Therefore, this seems to be a genuine false positive. Having analysed L = 1 false-positive target predictions, we turn our attention to confirmed L = 1 true positives. Figure  4  shows a representative example of a L = 1 true-positive target prediction. This is for Bexarotene (CHEMBL1023), an antineoplastic agent indicated by the FDA for Cutaneous T cell lymphoma.
Conclusion We have introduced and rigorously tested a suite of ligand-centric methods for target prediction exploiting an unusually large knowledge-base (887,435 ligand-target pairs, 504,755 database molecules and 4167 macromolecular targets). From here, we have verified that an approved drug has on average 11. 5 known targets below   ChEMBL820 ) is an alkylating agent. All these hits bind to the predicted target, Carbonic anhydrase 2 (target CHEMBL205), and hence this is a L = 1 target prediction. Since Carbonic anhydrase 2 is not a known target of Busulfan, this is one of the seven L = 1 false positives. This seems to be a genuine false positive due to the relatively low similarity of the hits to the query molecule (56. 5-31. 5%). 10 µM. This represents a substantially higher degree of drug polypharmacology than what is obtained using substantially less data  7, 45.
Methods Data provenance. We downloaded release 20 of the ChEMBL database  43  as a PostgreSQL dump, which contains data for 10,774 targets, 1,456,020 ligand molecules with disclosed chemical structure and 13,520,737 bioactivities curated from 59,610 scientific publications. Relevant data was retrieved using PostgreSQL 9. 4. 3 queries and all further processing was done with Python 2. 7. 9. As usual, single-atom fragments were removed and the largest molecular fragment of each compound in a salt form was kept. Next, we generated two separated tables from this data set, one for the query molecules and another for database molecules, each with their canonical SMILES, ChEMBL IDs and annotated targets.
The latter means that only molecular targets were considered, i. e. those with direct protein complex subunits assigned, homologous single protein target assigned and direct single protein target assigned (confidence score 7, 8 and 9, respectively). Generating data sets for the benchmark. These data sets are derived from the expanded knowledge-base. To study how method performance changes with the definitions of known targets, we focused on the same 745 approved drugs that were used as query molecules (test set) in a previous study  7. These molecules collectively contain 1,427 targets spanning 8,535 drug-targets pairs. The database molecules (training set) were formed by all the molecules passing the filters above except for the 745 drugs, which were removed to avoid any overlap between query and database molecules.