A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

Journal of Molecular Graphics and Modelling

Volumn 26, Issue 8, 2008, Pages 1276-1286

  Han, L Y ^a   Ma, X H ^a   Lin, H H ^a   Jia, J ^a   Zhu, F ^a   Xue, Y ^c   Li, Z R ^c   Cao, Z W ^b   Ji, Z L ^d   Chen, Y Z ^a,b  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b SHANGHAI CENTER FOR BIOINFORMATION TECHNOLOGY   (China)

^c SICHUAN UNIVERSITY   (China)

^d XIAMEN UNIVERSITY   (China)

Author keywords

Computer aided dug design; Drug discovery; High throughput screening; Lead discovery; Machine learning method; Virtual screening

Indexed keywords

DRUG PRODUCTS; LIGANDS; PROTEINS; VIRTUAL REALITY;

COMPUTER AIDED DUG DESIGN; HIGH-THROUGHPUT SCREENING; LEAD DISCOVERY; MACHINE LEARNING METHOD; VIRTUAL SCREENING;

SUPPORT VECTOR MACHINES;

CYTOCHROME P450 INHIBITOR; DIHYDROFOLATE REDUCTASE INHIBITOR; DOPAMINE RECEPTOR BLOCKING AGENT; ENZYME INHIBITOR; PROTEINASE INHIBITOR; SELECTIVE ESTROGEN RECEPTOR MODULATOR;

ARTICLE; COMPARATIVE STUDY; DATA BASE; HYDROPHOBICITY; LIBRARY; METHODOLOGY; PHARMACOPHORE; PHYSICAL CHEMISTRY; PRIORITY JOURNAL; STRUCTURE ANALYSIS;

ARTIFICIAL INTELLIGENCE; CENTRAL NERVOUS SYSTEM AGENTS; CHEMISTRY, PHYSICAL; DOPAMINE ANTAGONISTS; DRUG DESIGN; FOLIC ACID ANTAGONISTS; HIV PROTEASE INHIBITORS; HYDROPHOBICITY; MOLECULAR STRUCTURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 43049157546     PISSN: 10933263     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmgm.2007.12.002     Document Type: Article

References (87)

1. Shoichet B.K. Virtual screening of chemical libraries. Nature 432 (2004) 862-865
2. Lengauer T., Lemmen C., Rarey M., and Zimmermann M. Novel technologies for virtual screening. Drug Discov. Today 9 (2004) 27-34
3. Davies J.W., Glick M., and Jenkins J.L. Streamlining lead discovery by aligning in silico and high-throughput screening. Curr. Opin. Chem. Biol. 10 (2006) 343-351
4. Willett P. Similarity-based virtual screening using 2D fingerprints. Drug Discov. Today 11 (2006) 1046-1053
5. van de Waterbeemd H., and Gifford E. ADMET in silico modelling: towards prediction paradise?. Nat. Rev. Drug Discov. 2 (2003) 192-204
6. Matthew W.B., and Trotter S.B.H. Support vector machines for ADME property classification. QSAR & Combinatorial Sci. 22 (2003) 533-548
7. Harper G., Bradshaw J., Gittins J.C., Green D.V., and Leach A.R. Prediction of biological activity for high-throughput screening using binary kernel discrimination. J. Chem. Inf. Comput. Sci. 41 (2001) 1295-1300
8. Jorissen R.N., and Gilson M.K. Virtual screening of molecular databases using a support vector machine. J. Chem. Inf. Model. 45 (2005) 549-561
9. Glick M., Jenkins J.L., Nettles J.H., Hitchings H., and Davies J.W. Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and laplacian-modified naive bayesian classifiers. J. Chem. Inf. Model. 46 (2006) 193-200
10. Li H., Ung C.Y., Yap C.W., Xue Y., Li Z.R., and Chen Y.Z. Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods. J. Mol. Graph Model. 25 (2006) 313-323
11. Lepp Z., Kinoshita T., and Chuman H. Screening for new antidepressant leads of multiple activities by support vector machines. J. Chem. Inf. Model. 46 (2006) 158-167
12. Chen B., Harrison R.F., Papadatos G., Willett P., Wood D.J., Lewell X.Q., Greenidge P., and Stiefl N. Evaluation of machine-learning methods for ligand-based virtual screening. J. Comput. Aid. Mol. Des. (2007)
13. Hert J., Willett P., Wilton D.J., Acklin P., Azzaoui K., Jacoby E., and Schuffenhauer A. New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching. J. Chem. Inf. Model. 46 (2006) 462-470
14. Franke L., Byvatov E., Werz O., Steinhilber D., Schneider P., and Schneider G. Extraction and visualization of potential pharmacophore points using support vector machines: application to ligand-based virtual screening for COX-2 inhibitors. J. Med. Chem. 48 (2005) 6997-7004
15. Ghosh S., Nie A., An J., and Huang Z. Structure-based virtual screening of chemical libraries for drug discovery. Curr. Opin. Chem. Biol. 10 (2006) 194-202
16. Shoichet B.K., McGovern S.L., Wei B., and Irwin J.J. Lead discovery using molecular docking. Curr. Opin. Chem. Biol. 6 (2002) 439-446
17. Jansen J.M., and Martin E.J. Target-biased scoring approaches and expert systems in structure-based virtual screening. Curr. Opin. Chem. Biol. 8 (2004) 359-364
18. Mozziconacci J.C., Arnoult E., Bernard P., Do Q.T., Marot C., and Morin-Allory L. Optimization and validation of a docking-scoring protocol: application to virtual screening for COX-2 inhibitors. J. Med. Chem. 48 (2005) 1055-1068
19. Vidal D., Thormann M., and Pons M. A novel search engine for virtual screening of very large databases. J. Chem. Inf. Model. 46 (2006) 836-843
20. Cummings M.D., DesJarlais R.L., Gibbs A.C., Mohan V., and Jaeger E.P. Comparison of automated docking programs as virtual screening tools. J. Med. Chem. 48 (2005) 962-976
21. Evers A., and Klabunde T. Structure-based drug discovery using GPCR homology modeling: successful virtual screening for antagonists of the alpha1A adrenergic receptor. J. Med. Chem. 48 (2005) 1088-1097
22. Lorber D.M., and Shoichet B.K. Hierarchical docking of databases of multiple ligand conformations. Curr. Top Med. Chem. 5 (2005) 739-749
23. Stiefl N., and Zaliani A. A knowledge-based weighting approach to ligand-based virtual screening. J. Chem. Inf. Model. 46 (2006) 587-596
24. Vangrevelinghe E., Zimmermann K., Schoepfer J., Portmann R., Fabbro D., and Furet P. Discovery of a potent and selective protein kinase CK2 inhibitor by high-throughput docking. J. Med. Chem. 46 (2003) 2656-2662
25. Doman T.N., McGovern S.L., Witherbee B.J., Kasten T.P., Kurumbail R., Stallings W.C., Connolly D.T., and Shoichet B.K. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J. Med. Chem. 45 (2002) 2213-2221
26. Enyedy I.J., Ling Y., Nacro K., Tomita Y., Wu X., Cao Y., Guo R., Li B., Zhu X., Huang Y., Long Y.Q., Roller P.P., Yang D., and Wang S. Discovery of small-molecule inhibitors of Bcl-2 through structure-based computer screening. J. Med. Chem. 44 (2001) 4313-4324
27. Oprea T.I., and Matter H. Integrating virtual screening in lead discovery. Curr. Opin. Chem. Biol. 8 (2004) 349-358
28. Bocker A., Schneider G., and Teckentrup A. NIPALSTREE: a new hierarchical clustering approach for large compound libraries and its application to virtual screening. J. Chem. Inf. Model. 46 (2006) 2220-2229
29. Schuster D., Maurer E.M., Laggner C., Nashev L.G., Wilckens T., Langer T., and Odermatt A. The discovery of new 11beta-hydroxysteroid dehydrogenase type 1 inhibitors by common feature pharmacophore modeling and virtual screening. J. Med. Chem. 49 (2006) 3454-3466
30. Steindl T., Laggner C., and Langer T. Human rhinovirus 3C protease: generation of pharmacophore models for peptidic and nonpeptidic inhibitors and their application in virtual screening. J. Chem. Inf. Model. 45 (2005) 716-724
31. Li H., Yap C.W., Ung C.Y., Xue Y., Li Z.R., Han L.Y., Lin H.H., and Chen Y.Z. Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. J. Pharm. Sci. 96 (2007) 2838-2860
32. Li H., Yap C.W., Xue Y., Li Z.R., Ung C.Y., Han L.Y., and Chen Y.Z. Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic or toxicological properties of pharmaceutical agents. Drug Dev. Res. 66 (2006) 245-259
33. Lipinski C., and Hopkins A. Navigating chemical space for biology and medicine. Nature 432 (2004) 855-861
34. Wilton D.J., Harrison R.F., Willett P., Delaney J., Lawson K., and Mullier G. Virtual screening using binary kernel discrimination: analysis of pesticide data. J. Chem. Inf. Model. 46 (2006) 471-477
35. Chen B., Harrison R.F., Pasupa K., Willett P., Wilton D.J., Wood D.J., and Lewell X.Q. Virtual screening using binary kernel discrimination: effect of noisy training data and the optimization of performance. J. Chem. Inf. Model. 46 (2006) 478-486
36. Alvarez J.C. High-throughput docking as a source of novel drug leads. Curr. Opin. Chem. Biol. 8 (2004) 365-370
37. Schapira M., Raaka B.M., Das S., Fan L., Totrov M., Zhou Z., Wilson S.R., Abagyan R., and Samuels H.H. Discovery of diverse thyroid hormone receptor antagonists by high-throughput docking. Proc. Natl. Acad. Sci. U.S.A. 100 (2003) 7354-7359
38. Lipinski C.A., Lombardo F., Dominy B.W., and Feeney P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 46 (2001) 3-26
39. Perola E. Minimizing false positives in kinase virtual screens. Proteins 64 (2006) 422-435
40. Cui J., Han L.Y., Lin H.H., Zhang H.L., Tang Z.Q., Zheng C.J., Cao Z.W., and Chen Y.Z. Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties. Mol. Immunol. 44 (2007) 866-877
41. Spaltenstein A., Kazmierski W.M., Miller J.F., and Samano V. Discovery of next generation inhibitors of HIV protease. Curr. Top Med. Chem. 5 (2005) 1589-1607
42. Then R.L. Antimicrobial dihydrofolate reductase inhibitors-achievements and future options: review. J. Chemother. 16 (2004) 3-12
43. McGuire J.J. Anticancer antifolates: current status and future directions. Curr. Pharm. Des. 9 (2003) 2593-2613
44. Linares G.E., Ravaschino E.L., and Rodriguez J.B. Progresses in the field of drug design to combat tropical protozoan parasitic diseases. Curr. Med. Chem. 13 (2006) 335-360
45. Serretti A., De Ronchi D., Lorenzi C., and Berardi D. New antipsychotics and schizophrenia: a review on efficacy and side effects. Curr. Med. Chem. 1s1 (2004) 343-358
46. Adler C.H., and Kumar R. Pharmacological and surgical options for the treatment of cervical dystonia. Neurology 55 (2000) S9-S14
47. Hain T.C., and Uddin M. Pharmacological treatment of vertigo. CNS Drugs 17 (2003) 85-100
48. Demol P., Ruoff H.J., and Weihrauch T.R. Rational pharmacotherapy of gastrointestinal motility disorders. Eur. J. Pediatr. 148 (1989) 489-495
49. H.P. Rang, M.M.D., J.M. Ritter Pharmacology, vol. Churchill Livingstone, 2001.
50. Sutherland J.J., O'Brien L.A., and Weaver D.F. Spline-fitting with a genetic algorithm: a method for developing classification structure-activity relationships. J. Chem. Inf. Comput. Sci. 43 (2003) 1906-1915
51. Xue Y., Yap C.W., Sun L.Z., Cao Z.W., Wang J.F., and Chen Y.Z. Prediction of P-glycoprotein substrates by a support vector machine approach. J. Chem. Inf. Comput. Sci. 44 (2004) 1497-1505
52. Bostrom J., Bohm M., Gundertofte K., and Klebe G. A 3D QSAR study on a set of dopamine D4 receptor antagonists. J. Chem. Inf. Comput. Sci. 43 (2003) 1020-1027
53. Cai C.Z., Han L.Y., Ji Z.L., Chen X., and Chen Y.Z. SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res. 31 (2003) 3692-3697
54. Han L.Y., Cai C.Z., Ji Z.L., Cao Z.W., Cui J., and Chen Y.Z. Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach. Nucleic Acids Res. 32 (2004) 6437-6444
55. Lin H.H., Han L.Y., Cai C.Z., Ji Z.L., and Chen Y.Z. Prediction of transporter family from protein sequence by support vector machine approach. Proteins 62 (2006) 218-231
56. JK, W. JOELib/JOELib2. ed. 2005.
57. L.Y. Han, C.J. Zheng, B. Xie, J. Jia, X.H. Ma, F. Zhu, H.H. Lin, X. Chen, Y.Z. Chen, Support vector machine approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness. Drug Discov. Today (in press).
58. Oprea T.I., and Gottfries J. Chemography: the art of navigating in chemical space. J. Comb. Chem. 3 (2001) 157-166
59. Reymond, T.F.A.J.-L., Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. J. Chem. Inf. Model. 2007 (published on Web 30/01/2007).
60. Koch M.A., Schuffenhauer A., Scheck M., Wetzel S., Casaulta M., Odermatt A., Ertl P., and Waldmann H. Charting biologically relevant chemical space: a structural classification of natural products (SCONP). Proc. Natl. Acad. Sci. U.S.A. 102 (2005) 17272-17277
61. Fang H., Tong W., Shi L.M., Blair R., Perkins R., Branham W., Hass B.S., Xie Q., Dial S.L., Moland C.L., and Sheehan D.M. Structure-activity relationships for a large diverse set of natural, synthetic, and environmental estrogens. Chem. Res. Toxicol. 14 (2001) 280-294
62. Tong W., Xie Q., Hong H., Shi L., Fang H., and Perkins R. Assessment of prediction confidence and domain extrapolation of two structure-activity relationship models for predicting estrogen receptor binding activity. Environ. Health Perspect. 112 (2004) 1249-1254
63. Jacobs M.N. In silico tools to aid risk assessment of endocrine disrupting chemicals. Toxicology 205 (2004) 43-53
64. Hu J.Y., and Aizawa T. Quantitative structure-activity relationships for estrogen receptor binding affinity of phenolic chemicals. Water Res. 37 (2003) 1213-1222
65. Byvatov E., Fechner U., Sadowski J., and Schneider G. Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J. Chem. Inf. Comput. Sci. 43 (2003) 1882-1889
66. Doniger S., Hofman T., and Yeh J. Predicting CNS permeability of drug molecules: comparison of neural network and support vector machine algorithms. J. Computat. Biol. 9 (2002) 849-864
67. He L., Jurs P.C., Custer L.L., Durham S.K., and Pearl G.M. Predicting the genotoxicity of polycyclic aromatic compounds from molecular structure with different classifiers. Chem. Res. Toxicol. 16 (2003) 1567-1580
68. Snyder R.D., Pearl G.S., Mandakas G., Choy W.N., Goodsaid F., and Rosenblum I.Y. Assessment of the sensitivity of the computational programs DEREK, TOPKAT, and MCASE in the prediction of the genotoxicity of pharmaceutical molecules. Environ. Mol. Mutagen. 43 (2004) 143-158
69. Xue Y., Li Z.R., Yap C.W., Sun L.Z., Chen X., and Chen Y.Z. Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents. J. Chem. Inf. Comput. Sci. 44 (2004) 1630-1638
70. Yap C.W., Cai C.Z., Xue Y., and Chen Y.Z. Prediction of torsade-causing potential of drugs by support vector machine approach. Toxicol. Sci. 79 (2004) 170-177
71. Yap C.W., and Chen Y.Z. Quantitative structure-pharmacokinetic relationships for drug distribution properties by using general regression neural network. J. Pharm. Sci. 94 (2005) 153-168
72. Zernov V.V., Balakin K.V., Ivaschenko A.A., Savchuk N.P., and Pletnev I.V. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J. Chem. Inf. Comput. Sci. 43 (2003) 2048-2056
73. Li H., Yap C.W., Ung C.Y., Xue Y., Cao Z.W., and Chen Y.Z. Effect of selection of molecular descriptors on the prediction of blood-brain barrier penetrating and nonpenetrating agents by statistical learning methods. J. Chem. Inf. Model. 45 (2005) 1376-1384
74. Perez J.J. Managing molecular diversity. Chem. Soc. Rev. 34 (2005) 143-152
75. Willett P., Barnard J.M., and Downs G.M. Chemical similarity searching. J. Chem. Inf. Comput. Sci. 38 (1998) 983-996
76. Potter T., and Matter H. Random or rational design? Evaluation of diverse compound subsets from chemical structure databases. J. Med. Chem. 41 (1998) 478-488
77. Yap C.W., and Chen Y.Z. Prediction of cytochrome P450 3A4, 2D6, and 2C9 inhibitors and substrates by using support vector machines. J. Chem. Inf. Model. 45 (2005) 982-992
78. Grover I.I., Singh I.I., and Bakshi I.I. Quantitative structure-property relationships in pharmaceutical research-Part 2. Pharm. Sci. Technol. Today 3 (2000) 50-57
79. Rücker G.R.C. Counts of all walks as atomic and molecular descriptors. J. Chem. Inf. Comput. Sci. 33 (1993) 683-695
80. Pirard B., Brendel J., and Peukert S. The discovery of Kv1.5 blockers as a case study for the application of virtual screening approaches. J. Chem. Inf. Model. 45 (2005) 477-485
81. Rella M., Rushworth C.A., Guy J.L., Turner A.J., Langer T., and Jackson R.M. Structure-based pharmacophore design and virtual screening for novel angiotensin converting enzyme 2 inhibitors. J. Chem. Inf. Model. 46 (2006) 708-716
82. Ung C.Y., Li H., Yap C.W., and Chen Y.Z. In silico prediction of pregnane X receptor activators by machine learning approaches. Mol. Pharmacol. 71 (2007) 158-168
83. Whittle M., Gillet V.J., Willett P., and Loesel J. Analysis of data fusion methods in virtual screening: similarity and group fusion. J. Chem. Inf. Model. 46 (2006) 2206-2219
84. PubMed. In National Library of Medicine.
85. MICROMEDEX MICROMEDEX. In MICROMEDEX, Edition expires 12/2003, Greenwood Village, Colorado.
86. Bethesda AHFS drug information, vol. American Society of Health-System Pharmacists Inc., 2001.
87. Li H., Ung C., Yap C., Xue Y., Li Z., Cao Z., and Chen Y. Prediction of genotoxicity of chemical compounds by statistical learning methods. Chem. Res. Toxicol. 18 (2005) 1071-1080