{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T23:12:47Z","timestamp":1772925167872,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2015,4,15]],"date-time":"2015-04-15T00:00:00Z","timestamp":1429056000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2015,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The rampant increase of public bioactivity databases has fostered the development of computational chemogenomics methodologies to evaluate potential ligand-target interactions (polypharmacology) both in a qualitative and quantitative way. Bayesian target prediction algorithms predict the probability of an interaction between a compound and a panel of targets, thus assessing compound polypharmacology qualitatively, whereas structure-activity relationship techniques are able to provide quantitative bioactivity predictions. We propose an integrated drug discovery pipeline combining <jats:italic>in silico<\/jats:italic> target prediction and proteochemometric modelling (PCM) for the respective prediction of compound polypharmacology and potency\/affinity. The proposed pipeline was evaluated on the retrospective discovery of <jats:italic>Plasmodium falciparum<\/jats:italic> DHFR inhibitors. The qualitative <jats:italic>in silico<\/jats:italic> target prediction model comprised 553,084 ligand-target associations (a total of 262,174 compounds), covering 3,481 protein targets and used protein domain annotations to extrapolate predictions across species. The prediction of bioactivities for plasmodial DHFR led to a recall value of 79% and a precision of 100%, where the latter high value arises from the structural similarity of plasmodial DHFR inhibitors and <jats:italic>T. gondii<\/jats:italic> DHFR inhibitors in the training set. Quantitative PCM models were then trained on a dataset comprising 20 eukaryotic, protozoan and bacterial DHFR sequences, and 1,505 distinct compounds (in total 3,099 data points). The most predictive PCM model exhibited <jats:italic>R<\/jats:italic>\n            <jats:sup>2<\/jats:sup>\n            <jats:sub>\n              <jats:italic>0<\/jats:italic>\n            <\/jats:sub>\n            <jats:sub>test<\/jats:sub> and RMSE<jats:sub>test<\/jats:sub> values of 0.79 and 0.59 pIC<jats:sub>50<\/jats:sub> units respectively, which was shown to outperform models based exclusively on compound (<jats:italic>R<\/jats:italic>\n            <jats:sup>2<\/jats:sup>\n            <jats:sub>\n              <jats:italic>0<\/jats:italic>\n            <\/jats:sub>\n            <jats:sub>test<\/jats:sub>\/RMSE<jats:sub>test<\/jats:sub>\u2009=\u20090.63\/0.78) and target information (<jats:italic>R<\/jats:italic>\n            <jats:sup>2<\/jats:sup>\n            <jats:sub>\n              <jats:italic>0<\/jats:italic>\n            <\/jats:sub>\n            <jats:sub>test<\/jats:sub>\/RMSE<jats:sub>test<\/jats:sub>\u2009=\u20090.09\/1.22), as well as inductive transfer knowledge between targets, with respective <jats:italic>R<\/jats:italic>\n            <jats:sup>2<\/jats:sup>\n            <jats:sub>\n              <jats:italic>0<\/jats:italic>\n            <\/jats:sub>\n            <jats:sub>test<\/jats:sub> and RMSE<jats:sub>test<\/jats:sub> values of 0.76 and 0.63 pIC<jats:sub>50<\/jats:sub> units. Finally, both methods were integrated to predict the protein targets and the potency on plasmodial DHFR for the GSK TCAMS dataset, which comprises 13,533 compounds displaying strong anti-malarial activity. 534 of those compounds were identified as DHFR inhibitors by the target prediction algorithm, while the PCM algorithm identified 25 compounds, and 23 compounds (predicted pIC<jats:sub>50<\/jats:sub>\u2009&gt;\u20097) were identified by both methods. Overall, this integrated approach simultaneously provides target and potency\/affinity predictions for small molecules.<\/jats:p>","DOI":"10.1186\/s13321-015-0063-9","type":"journal-article","created":{"date-parts":[[2015,4,14]],"date-time":"2015-04-14T07:35:23Z","timestamp":1428996923000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity\/potency of small molecules"],"prefix":"10.1186","volume":"7","author":[{"given":"Shardul","family":"Paricharak","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isidro","family":"Cort\u00e9s-Ciriano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adriaan P","family":"IJzerman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Th\u00e9r\u00e8se E","family":"Malliavin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Bender","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,4,15]]},"reference":[{"key":"63_CR1","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1039\/C2MD20242E","volume":"4","author":"X Jalencas","year":"2013","unstructured":"Jalencas X, Mestres J. On the origins of drug polypharmacology. Med Chem Comm. 2013;4:80.","journal-title":"Med Chem Comm"},{"key":"63_CR2","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1038\/nature11159","volume":"486","author":"E Lounkine","year":"2012","unstructured":"Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J, Jenkins JL, et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature. 2012;486:361\u20137.","journal-title":"Nature"},{"key":"63_CR3","doi-asserted-by":"crossref","unstructured":"Cortes-Ciriano I, Ain QU, Subramanian V, Lenselink EB, Mendez-Lucio O, IJzerman AP, et al. Polypharmacology modelling using proteochemometrics: recent developments and future prospects. Med Chem Comm. 2015;6:24\u201350 doi: 10.1039\/C4MD00216D.","DOI":"10.1039\/C4MD00216D"},{"key":"63_CR4","first-page":"42","volume":"5","author":"GJ Van Westen","year":"2013","unstructured":"Van Westen GJ, Swier RF, Cortes-Ciriano I, Wegner JK, Overington JP, IJzerman AP, et al. Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets. J Chem inform. 2013;5:42.","journal-title":"J Chem inform"},{"key":"63_CR5","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1080\/10629360601054032","volume":"18","author":"V Poroikov","year":"2007","unstructured":"Poroikov V, Filimonov D, Lagunin A, Gloriozova T, Zakharov A. PASS: identification of probable targets and mechanisms of toxicity\u2020. SAR QSAR Env Res. 2007;18:101\u201310.","journal-title":"SAR QSAR Env Res"},{"key":"63_CR6","doi-asserted-by":"publisher","first-page":"1124","DOI":"10.1021\/ci060003g","volume":"46","author":"Nidhi","year":"2006","unstructured":"Nidhi, Glick M, Davies JW, Jenkins JL. Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases. J Chem Inf Model. 2006;46:1124\u201333.","journal-title":"J Chem Inf Model"},{"key":"63_CR7","doi-asserted-by":"publisher","first-page":"2313","DOI":"10.1021\/ci800079x","volume":"48","author":"F Nigsch","year":"2008","unstructured":"Nigsch F, Bender A, Jenkins JL, Mitchell JBO. Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics. J Chem Inf Model. 2008;48:2313\u201325.","journal-title":"J Chem Inf Model"},{"key":"63_CR8","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1038\/nbt1284","volume":"25","author":"MJ Keiser","year":"2007","unstructured":"Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK. Relating protein pharmacology by ligand chemistry. Nat Biotechnol. 2007;25:197\u2013206.","journal-title":"Nat Biotechnol"},{"key":"63_CR9","doi-asserted-by":"publisher","first-page":"2190","DOI":"10.1021\/ci9000376","volume":"49","author":"N Wale","year":"2010","unstructured":"Wale N, Karypis G. Target Fishing for Chemical Compounds using Target-Ligand Activity data and Ranking based Methods. J Chem Inf Model. 2010;49:2190\u2013201.","journal-title":"J Chem Inf Model"},{"key":"63_CR10","doi-asserted-by":"publisher","first-page":"1957","DOI":"10.1021\/ci300435j","volume":"53","author":"A Koutsoukas","year":"2013","unstructured":"Koutsoukas A, Lowe R, KalantarMotamedi Y, Mussa HY, Klaffke W, Mitchell JBO, et al. In Silico Target Predictions: Defining a Benchmarking Data Set and Comparison of Performance of the Multiclass Na\u00efve Bayes and Parzen-Rosenblatt Window. J Chem Inf Model. 2013;53:1957\u201366.","journal-title":"J Chem Inf Model"},{"key":"63_CR11","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1039\/C0MD00165A","volume":"2","author":"GJP Van Westen","year":"2011","unstructured":"Van Westen GJP, Wegner JKJ, IJzerman AP, van Vlijmen HWT, Bender A. Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets. Med Chem Comm. 2011;2:16\u201330.","journal-title":"Med Chem Comm"},{"key":"63_CR12","first-page":"210","volume":"46","author":"P Perlmann","year":"2000","unstructured":"Perlmann P, Troye-Blomberg M. Malaria blood-stage infection and its conyrol by the immune system. Folia Biol (Praha). 2000;46:210\u20138.","journal-title":"Folia Biol (Praha)"},{"key":"63_CR13","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/S0163-7258(00)00115-7","volume":"89","author":"P Olliaro","year":"2001","unstructured":"Olliaro P. Mode of action and mechanisms of resistance for antimalarial drugs. Pharmacol Ther. 2001;89:207\u201319.","journal-title":"Pharmacol Ther"},{"key":"63_CR14","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1007\/s10822-012-9618-2","volume":"26","author":"D Hecht","year":"2012","unstructured":"Hecht D, Fogel GB. Modeling the evolution of drug resistance in malaria. J Comput Aided Mol Des. 2012;26:1343\u201353.","journal-title":"J Comput Aided Mol Des"},{"key":"63_CR15","volume-title":"The George Institute for International Health","author":"M Moran","year":"2007","unstructured":"Moran M, Guzman J, Ropars A-L. The malaria product pipeline: planning for the future. In: The George Institute for International Health. 2007."},{"key":"63_CR16","unstructured":"ChEMBL - Neglected Tropical Disease. http:\/\/www.ebi.ac.uk\/chemblntd"},{"key":"63_CR17","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1038\/nature09107","volume":"465","author":"F-J Gamo","year":"2010","unstructured":"Gamo F-J, Sanz LM, Vidal J, de Cozar C, Alvarez E, Lavandera J-L, et al. Thousands of chemical starting points for antimalarial lead identification. Nature. 2010;465:305\u201310.","journal-title":"Nature"},{"key":"63_CR18","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1186\/1471-2105-9-201","volume":"9","author":"R Verma","year":"2008","unstructured":"Verma R, Tiwari A, Kaur S, Varshney GC, Raghava GPS. Identification of proteins secreted by malaria parasite into erythrocyte using SVM and PSSM profiles. BMC Bio inform. 2008;9:201.","journal-title":"BMC Bio inform"},{"key":"63_CR19","first-page":"2105","volume":"14","author":"S Jamal","year":"2013","unstructured":"Jamal S, Periwal V, Scaria V. Predictive modeling of anti-malarial molecules inhibiting apicoplast formation. BMC Bio inform. 2013;14:2105\u201314.","journal-title":"BMC Bio inform"},{"key":"63_CR20","doi-asserted-by":"publisher","first-page":"898","DOI":"10.2174\/138620711797537058","volume":"14","author":"S Subramaniam","year":"2011","unstructured":"Subramaniam S, Mehrotra M, Gupta D. Support Vector Machine Based Prediction of P. falciparum Proteasome Inhibitors and Development of Focused Library by Molecular Docking. Comb Chem High Throughput Screen. 2011;14:898\u2013907.","journal-title":"Comb Chem High Throughput Screen"},{"key":"63_CR21","unstructured":"Vortex D: v2013.03.20719. 2013."},{"issue":"Database issue","key":"63_CR22","doi-asserted-by":"publisher","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","volume":"40","author":"A Gaulton","year":"2012","unstructured":"Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40(Database issue):D1100\u20137.","journal-title":"Nucleic Acids Res"},{"key":"63_CR23","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1038\/nchembio.354","volume":"6","author":"A Bender","year":"2010","unstructured":"Bender A. Databases: Compound bioactivities go public. Nat Chem Biol. 2010;6:309.","journal-title":"Nat Chem Biol"},{"key":"63_CR24","volume-title":"Standardizer","author":"ChemAxon","year":"2013","unstructured":"ChemAxon. Standardizer. 2013."},{"issue":"Database issue","key":"63_CR25","doi-asserted-by":"publisher","first-page":"D306","DOI":"10.1093\/nar\/gkr948","volume":"40","author":"S Hunter","year":"2012","unstructured":"Hunter S, Jones P, Mitchell A, Apweiler R, Attwood TK, Bateman A, et al. InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acid Res. 2012;40(Database issue):D306\u201312.","journal-title":"Nucleic Acid Res"},{"key":"63_CR26","doi-asserted-by":"publisher","first-page":"D43","DOI":"10.1093\/nar\/gks1068","volume":"41","author":"The Uniprot Consortium","year":"2013","unstructured":"The Uniprot Consortium. Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucleic Acid Res. 2013;41:D43\u20137.","journal-title":"Nucleic Acid Res"},{"key":"63_CR27","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1021\/ci034207y","volume":"44","author":"A Bender","year":"2004","unstructured":"Bender A, Mussa HY, Glen RC. Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Na\u00efve Bayesian Classifier. J Chem Inf Model. 2004;44:170\u20138.","journal-title":"J Chem Inf Model"},{"key":"63_CR28","doi-asserted-by":"publisher","first-page":"1708","DOI":"10.1021\/ci0498719","volume":"44","author":"A Bender","year":"2004","unstructured":"Bender A, Mussa HY, Glen RC. Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D): Evaluation of Performance. J Chem Inf Model. 2004;44:1708\u201318.","journal-title":"J Chem Inf Model"},{"key":"63_CR29","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1021\/ci100062n","volume":"50","author":"M Sastry","year":"2010","unstructured":"Sastry M, Lowrie JF, Dixon SL, Sherman W. Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J Chem Inf Model. 2010;50:771\u201384.","journal-title":"J Chem Inf Model"},{"key":"63_CR30","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/1752-153X-2-5","volume":"2","author":"NM O\u2019Boyle","year":"2008","unstructured":"O\u2019Boyle NM, Morley C, Hutchison GR. Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit. Chem Cent J. 2008;2:5\u201311.","journal-title":"Chem Cent J"},{"key":"63_CR31","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1021\/ci6005504","volume":"47","author":"TJ Crisman","year":"2007","unstructured":"Crisman TJ, Parker CN, Jenkins JL, Scheiber J, Thoma M, Kang Z, et al. Understanding false positives in reporter gene assays: in silico chemogenimics approaches to prioritize cell-based HTS data. J Chem Inf Model. 2007;47:1319\u201327.","journal-title":"J Chem Inf Model"},{"key":"63_CR32","doi-asserted-by":"publisher","first-page":"2575","DOI":"10.1021\/pr900107z","volume":"8","author":"A Bender","year":"2009","unstructured":"Bender A, Mikhailov D, Glick M, Scheiber J, Davies JW, Cleaver S, et al. Use of Ligand Based Models for Protein Domains To Predict Novel Molecular Targets and Applications To Triage Affinity Chromatography Data. J Proteome Res. 2009;8:2575\u201385.","journal-title":"J Proteome Res"},{"key":"63_CR33","doi-asserted-by":"publisher","first-page":"2788","DOI":"10.1021\/pr8010843","volume":"8","author":"P Prathipati","year":"2009","unstructured":"Prathipati P, Ma NL, Manjunatha UH, Bender A. Fishing the Target of Antitubercular Compounds: In Silico Target Deconvolution Model Development and Validation. J Proteome Res. 2009;8:2788\u201398.","journal-title":"J Proteome Res"},{"key":"63_CR34","volume-title":"Chemistry Aware Model Builder (camb): an R Package for Predictive Bioactivity Modeling","author":"DS Murrell","year":"2014","unstructured":"Murrell DS, Cortes-Ciriano I, van Westen GJP, Stott IP, Malliavin T, Bender A, et al. Chemistry Aware Model Builder (camb): an R Package for Predictive Bioactivity Modeling. 2014. http:\/\/github.com\/cambDI\/camb."},{"key":"63_CR35","doi-asserted-by":"publisher","first-page":"13897","DOI":"10.1021\/bi971711l","volume":"36","author":"V Cody","year":"1997","unstructured":"Cody V, Galitsky N, Luft JR, Pangborn W, Rosowsky A, Blakley RL. Comparison of two independent crystal structures of human dihydrofolate reductase ternary complexes reduced with nicotinamide adenine dinucleotide phosphate and the very tight-binding inhibitor PT523. Biochemistry. 1997;36:13897\u2013903.","journal-title":"Biochemistry"},{"key":"63_CR36","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1038\/msb.2011.75","volume":"7","author":"F Sievers","year":"2011","unstructured":"Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011;7:539.","journal-title":"Mol Syst Biol"},{"key":"63_CR37","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1002\/cem.1290","volume":"24","author":"V Consonni","year":"2010","unstructured":"Consonni V, Ballabio D, Todeschini R. Evaluation of model predictive ability by external validation techniques. J Chemometr. 2010;24:194\u2013201.","journal-title":"J Chemometr"},{"key":"63_CR38","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1021\/ci025626i","volume":"43","author":"DM Hawkins","year":"2003","unstructured":"Hawkins DM, Basak SC, Mills D. Assessing Model Fit by Cross-Validation. J Chem Inform Comput Sci. 2003;43:579\u201386.","journal-title":"J Chem Inform Comput Sci"},{"key":"63_CR39","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1002\/qsar.200390007","volume":"22","author":"A Tropsha","year":"2003","unstructured":"Tropsha A, Gramatica P, Gombar V. The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models. QSAR Comb Sci. 2003;22:69\u201377.","journal-title":"QSAR Comb Sci"},{"key":"63_CR40","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/S1093-3263(01)00123-1","volume":"20","author":"A Golbraikh","year":"2002","unstructured":"Golbraikh A, Tropsha A. Beware of q2! J Mol Graphics Modell. 2002;20:269\u201376.","journal-title":"J Mol Graphics Modell"},{"issue":"6","key":"63_CR41","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/s10822-014-9743-1","volume":"28","author":"JB Brown","year":"2014","unstructured":"Brown JB, Okuno Y, Marcou G, Varnek A, Horvath D. Computational chemogenomics: Is it more than inductive transfer? J Comput Aided Mol Des. 2014;28(6):597\u2013618.","journal-title":"J Comput Aided Mol Des"},{"key":"63_CR42","doi-asserted-by":"publisher","first-page":"e1000173","DOI":"10.1371\/journal.pcbi.1000173","volume":"4","author":"A Ben-Hur","year":"2008","unstructured":"Ben-Hur A, Ong C. Support vector machines and kernels for computational biology. PLoS Comput Biol. 2008;4:e1000173.","journal-title":"PLoS Comput Biol"},{"key":"63_CR43","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH. Greedy function approximation: A gradient boosting machine. Ann Stat. 2001;29:1189\u2013232.","journal-title":"Ann Stat"},{"key":"63_CR44","doi-asserted-by":"crossref","unstructured":"Rasmussen CE, Williams CKI. Gaussian Processes for Machine Learning, the MIT Press, 2006, ISBN 026218253X. c 2006 Massachusetts Institute of Technology.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"63_CR45","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random Forests. Mach Learning. 2001;45:5\u201332.","journal-title":"Mach Learning"},{"key":"63_CR46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","volume":"28","author":"M Kuhn","year":"2008","unstructured":"Kuhn M. Building Predictive Models in R Using the caret Package. J Stat Softw. 2008;28:1\u201326.","journal-title":"J Stat Softw"},{"key":"63_CR47","doi-asserted-by":"publisher","first-page":"e1003257","DOI":"10.1371\/journal.pcbi.1003257","volume":"9","author":"A Spitzm\u00fcller","year":"2013","unstructured":"Spitzm\u00fcller A, Mestres J. Prediction of the P. falciparum target space relevant to malaria drug discovery. PLoS Comput Biol. 2013;9:e1003257.","journal-title":"PLoS Comput Biol"},{"key":"63_CR48","doi-asserted-by":"publisher","first-page":"e1003253","DOI":"10.1371\/journal.pcbi.1003253","volume":"9","author":"F Mart\u00ednez-Jim\u00e9nez","year":"2013","unstructured":"Mart\u00ednez-Jim\u00e9nez F, Papadatos G, Yang L, Wallace IM, Kumar V, Pieper U, et al. Target prediction for an open access set of compounds active against Mycobacterium tuberculosis. PLoS Comput Biol. 2013;9:e1003253.","journal-title":"PLoS Comput Biol"},{"key":"63_CR49","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/s10822-004-4077-z","volume":"18","author":"R Clark","year":"2004","unstructured":"Clark R, Fox P. Statistical variation in progressive scrambling. J Comput Aided Mol Des. 2004;18:563\u201376.","journal-title":"J Comput Aided Mol Des"},{"key":"63_CR50","doi-asserted-by":"publisher","first-page":"16823","DOI":"10.1073\/pnas.1204556109","volume":"109","author":"Y Yuthavong","year":"2012","unstructured":"Yuthavong Y, Tarnchompoo B, Vilaivan T, Chitnumsub P, Kamchonwongpaisan S, Charman SA, et al. Malarial dihydrofolate reductase as a paradigm for drug development against a resistance-compromised target. Proc Natl Acad Sci U S A. 2012;109:16823\u20138.","journal-title":"Proc Natl Acad Sci U S A"},{"key":"63_CR51","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1002\/med.20082","volume":"26","author":"K Ersmark","year":"2006","unstructured":"Ersmark K, Samuelsson B, Hallberg A. Plasmepsins as Potential Targets for New Antimalarial Therapy. Med Res Rev. 2006;26:626\u201366.","journal-title":"Med Res Rev"},{"key":"63_CR52","doi-asserted-by":"publisher","first-page":"408","DOI":"10.2174\/156802612799362913","volume":"12","author":"M Marco","year":"2012","unstructured":"Marco M, Coter\u00f3n JM. Falcipain inhibition as a promising antimalarial target. Curr Top Med Chem. 2012;12:408\u201344.","journal-title":"Curr Top Med Chem"},{"key":"63_CR53","doi-asserted-by":"publisher","first-page":"292","DOI":"10.2174\/156802609788085313","volume":"9","author":"KT Andrews","year":"2009","unstructured":"Andrews KT, Tran TN, Wheatley NC, Fairlie DP. Targeting histone deacetylase inhibitors for anti-malarial therapy. Curr Top Med Chem. 2009;9:292\u2013308.","journal-title":"Curr Top Med Chem"},{"key":"63_CR54","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1039\/C2MD20286G","volume":"4","author":"I Cortes-Ciriano","year":"2013","unstructured":"Cortes-Ciriano I, Koutsoukas A, Abian O, Glen RC, Velazquez-Campoy A, Bender A. Experimental validation of in silico target predictions on synergistic protein targets. Med Chem Comm. 2013;4:278\u201388.","journal-title":"Med Chem Comm"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-015-0063-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13321-015-0063-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-015-0063-9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-015-0063-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T10:48:15Z","timestamp":1630579695000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-015-0063-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,4,15]]},"references-count":54,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2015,12]]}},"alternative-id":["63"],"URL":"https:\/\/doi.org\/10.1186\/s13321-015-0063-9","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,4,15]]},"assertion":[{"value":"18 November 2014","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2015","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2015","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"15"}}