Support vector machines for drug discovery

Expert Opinion on Drug Discovery

Volumn 9, Issue 1, 2014, Pages 93-104

  Heikamp, Kathrin ^a   Bajorath, Jürgen ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

Chemical biological property prediction; Compound activity prediction; Compound classification; Hybrid methods; Kernel functions; Machine learning; Regression modeling; Support vector machines; Virtual screening

Indexed keywords

ALGORITHM; CLASSIFICATION; CLASSIFIER; DRUG DEVELOPMENT; HUMAN; HYBRID; KERNEL METHOD; MACHINE LEARNING; PHYSICAL CHEMISTRY; PREDICTION; PRIORITY JOURNAL; REVIEW; SUPPORT VECTOR MACHINE; THEORY;

DRUG DISCOVERY; HUMANS; SUPPORT VECTOR MACHINES;

EID: 84890517122     PISSN: 17460441     EISSN: 1746045X     Source Type: Journal    
DOI: 10.1517/17460441.2014.866943     Document Type: Review

References (75)

1. Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20:273-97
2. Burbidge R, Trotter M, Buxton B, Holden S. Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Comput Chem 2001;26:5-14
3. Warmuth MK, Liao J, Ratsch G, et al. Active learning with support vector machines in the drug discovery process. J Chem Inf Comput Sci 2003;43:667-73
4. Byvatov E, Fechner U, Sadowski J, Schneider G. Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J Chem Inf Comput Sci 2003;43:1882-9
5. Zernov VV, Balakin KV, Ivaschenko AA, et al. Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J Chem Inf Comput Sci 2003;43:2048-56
6. Saeh JC, Lyne PD, Takasaki BK, Cosgrove DA. Lead hopping using SVM and 3D pharmacophore fingerprints. J Chem Inf Model 2005;45:1122-33
7. Geppert H, Horváth T, Gartner T, et al. Support-vector-machine- based ranking significantly improves the effectiveness of similarity searching using 2D fingerprints and multiple reference compounds. J Chem Inf Model 2008;48:742-6. SVMs using fingerprint descriptors is shown to outperform fingerprint-based similarity searching.
8. Hinselmann G, Rosenbaum L, Jahn A, et al. Large-scale learning of structureactivity relationships using a linear support vector machine and problemspecific metrics. J Chem Inf Model 2011;51:203-13
9. Vapnik VN. The nature of statistical learning theory. 2nd edition. Springer; NY, USA: 2000
10. Burges CJC. A. Tutorial on Support Vector Machines for Pattern Recognition. Data Min Knowl Disc 1998;2:121-67. A useful SVM tutorial.
11. Jorissen RN, Gilson MK. Virtual screening of molecular databases using a support vector machine. J Chem Inf Model 2005;45:549-61. Original proposal of an SVM compound ranking strategy.
12. Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. In: Haussler D, editor. Proceedings of the 5th Annual Workshop on Computational Learning Theory. ACM; Pittsburgh, PA, USA; 1992. p. 144-52
13. Vert J-P, Jacob L. Machine learning for in silico virtual screening and chemical genomics: New strategies. Comb Chem High Throughput Screen 2008;11:677-85. Review of various kernel functions for chemogenomics.
14. Ben-Hur A, Weston J. A User's guide to support vector machines. Methods Mol Biol 2010;609:223-39
15. Smola AJ, Scholkopf B. A tutorial on support vector regression. Stat Comput 2004;14:199-222
16. Gartner T, Flach P, Wrobel S. On graph kernels: Hardness results and efficient alternatives. Learning theory and kernel machines. Springer; Heidelberg, Berlin; 2003
17. Kashima H, Tsuda K, Inokuchi A. Marginalized kernels between labeled graphs. In: Fawcett T, Mishra N, editors. Proceedings of the 20th International Conference on Machine Learning. The AAAI Press; Washington DC, USA; 2003
18. Mahé P, Ueda N, Akutsu T, et al. Graph kernels for molecular structureactivity relationship analysis with support vector machines. J Chem Inf Model 2005;45:939-51
19. Ralaivola L, Swamidass SJ, Saigo H, Baldi P. Graph kernels for chemical informatics. Neural Netw 2005;18:1093-110. Introduction of the Tanimoto kernel.
20. Mahé P, Ralaivola L, Stoven V, Vert J-P. The pharmacophore kernel for virtual screening with support vector machines. J Chem Inf Model 2006;46:2003-14
21. Mohr JA, Jain BJ, Obermayer K. Molecule kernels: A descriptor-and alignment-free quantitative structure-activity relationship approach. J Chem Inf Model 2008;48:1868-81
22. Azencott C-A, Ksikes A, Swamidass SJ, et al. One-to four-dimensional kernels for virtual screening and the prediction of physical, chemical, and biological properties. J Chem Inf Model 2007;47:965-74
23. Erhan D, L'heureux P-J, Yue SY, Bengio Y. Collaborative filtering on a family of biological targets. J Chem Inf Model 2006;46:626-35. Introduction of a target-ligand kernel.
24. Jacob L, Vert J-P. Protein-ligand interaction prediction: An improved chemogenomics approach. Bioinformatics 2008;24:2149-56
25. Meslamani J, Rognan D. Enhancing the accuracy of chemogenomic models with a three-dimensional binding site kernel. J Chem Inf Model 2011;51:1593-603
26. Wassermann AM, Heikamp K, Bajorath J. Potency-directed similarity searching using support vector machines. Chem Biol Drug Des 2011;77:30-8
27. Heikamp K, Hu X, Yan A, Bajorath J. Prediction of activity cliffs using support vector machines. J Chem Inf Model 2012;52:2354-65
28. Liew CY, Ma XH, Liu X, Yap CW. SVM model for virtual screening of Lck inhibitors. J Chem Inf Model 2009;49:877-85
29. Lv W, Xue Y. Prediction of acetylcholinesterase inhibitors and characterization of correlative molecular descriptors by machine learning methods. Eur J Med Chem 2010;45:1167-72
30. Sun H, Veith H, Xia M, et al. Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data. J Chem Inf Model 2011;51:2474-81
31. Agarwal S, Dugar D, Sengupta S. Ranking chemical structures for drug discovery: A new machine learning approach. J Chem Inf Model 2010;50:716-31
32. Rathke F, Hansen K, Brefeld U, Muller K-R. StructRank: A new approach for ligand-based virtual screening. J Chem Inf Model 2011;51:83-92
33. Sun H, Shahane S, Xia M, et al. Structure based model for the prediction of phospholipidosis induction potential of small molecules. J Chem Inf Model 2012;52:1798-805
34. Chen H, Carlsson L, Eriksson M, et al. Beyond the scope of Free-Wilson analysis: Building interpretable QSAR models with machine learning algorithms. J Chem Inf Model 2013;53:1324-36
35. Jacob L, Hoffmann B, Stoven V, Vert J-P. Virtual screening of GPCRs: An in silico chemogenomics approach. BMC Bioinformatics 2008;9:363
36. Wang Y-C, Zhang C-H, Deng N-Y, Wang Y. Kernel-based data fusion improves the drug-protein interaction prediction. Comput Biol Chem 2011;35:353-62
37. Wang F, Liu D, Wang H, et al. Computational screening for active compounds targeting protein sequences: Methodology and experimental validation. J Chem Inf Model 2011;51:2821-8
38. Wassermann AM, Geppert H, Bajorath J. Ligand prediction for orphan targets using support vector machines and various target-ligand kernels is dominated by nearest neighbor effects. J Chem Inf Model 2009;49:2155-67. Systematic analysis of different targetligand kernels revealing that orphan screening performance was dominated by compound similarity.
39. Geppert H, Humrich J, Stumpfe D, et al. Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint descriptors. J Chem Inf Model 2009;49:767-79
40. Wassermann AM, Geppert H, Bajorath J. Searching for target-selective compounds using different combinations of multiclass support vector machine ranking methods, kernel functions, and fingerprint descriptors. J Chem Inf Model 2009;49:582-92
41. Michielan L, Stephanie F, Terfloth L, et al. Exploring potency and selectivity receptor antagonist profiles using a multilabel classification approach: The human adenosine receptors as a key study. J Chem Inf Model 2009;49:2820-36
42. Heikamp K, Bajorath J. Prediction of compounds with closely related activity profiles using weighted support vector machine linear combinations. J Chem Inf Model 2013;53:791-801
43. Kawai K, Fujishima S, Takahashi Y. Predictive activity profiling of drugs by topological-fragment-spectra-based support vector machines. J Chem Inf Model 2008;48:1152-60
44. Sato T, Matsuo Y, Honma T, Yokoyama S. In silico functional profiling of small molecules and its applications. J Med Chem 2008;51:7705-16
45. Lind P, Maltseva T. Support vector machines for the estimation of aqueous solubility. J Chem Inf Comput Sci 2003;43:1855-9
46. Cheng T, Li Q, Wang Y, et al. Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection. J Chem Inf Model 2011;51:229-36
47. Shen J, Cheng F, Xu Y, et al. Estimation of ADME properties with substructure pattern recognition. J Chem Inf Model 2010;50:1034-41
48. Hou T, Wang J, Li Y. ADME evaluation in drug discovery. The prediction of human intestinal absorption by a support vector machine. J Chem Inf Model 2007;47:2408-15
49. Kumar R, Sharma A, Varadwaj PK. A prediction model for oral bioavailability of drugs using physicochemical properties by support vector machine. J Nat Sci Biol Med 2011;2:168-73
50. Bhavani S, Nagargadde A, Thawani A, et al. Substructure-based support vector machine classifiers for prediction of adverse effects in diverse classes of drugs. J Chem Inf Model 2006;46:2478-86
51. Zhang H, Li W, Xie Y, et al. Rapid and accurate assessment of seizure liability of drugs by using an optimal support vector machine method. Toxicol In Vitro 2011;25:1848-54
52. Hu X, Yan A. In silico models to discriminate compounds inducing and noninducing toxic myopathy. Mol Inform 2012;31:27-39
53. Su B-H, Tu Y, Esposito EX, Tseng YJ. Predictive toxicology modeling: Protocols for exploring hERG classification and Tetrahymena pyriformis end point predictions. J Chem Inf Model 2012;52:1660-73
54. Ding B, Li N, Wang W. Characterizing binding of small molecules. II. Evaluating the potency of small molecules to combat resistance based on docking structures. J Chem Inf Model 2013;53:1213-22
55. Podolyan Y, Walters M A, Karypis G. Assessing synthetic accessibility of chemical compounds using machine learning methods. J Chem Inf Model 2010;50:979-91
56. Muller K-R, Ratsch G, Sonnenburg S, et al. Classifying "drug-likeness" with kernel-based learning methods. J Chem Inf Model 2005;45:249-53
57. Kortagere S, Chekmarev D, Welsh WJ, Ekins S. New predictive models for blood-brain barrier permeability of druglike molecules. Pharm Res 2008;25:1836-45
58. Volkamer A, Kuhn D, Grombacher T, et al. Combining global and local measures for structure-based druggability predictions. J Chem Inf Model 2012;52:360-72
59. Li L, Wang B, Meroueh SO. Support vector regression scoring of receptorligand complexes for rank-ordering and virtual screening of chemical libraries. J Chem Inf Model 2011;51:2132-8
60. Li G-B, Yang L-L, Wang W-J, et al. IDScore: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein-Ligand Interactions. J Chem Inf Model 2013;53:592-600
61. Fechner N, Jahn A, Hinselmann G, et al. Estimation of the applicability domain of kernel-based machine learning models for virtual screening. J Cheminform 2010;2:2
62. Nagamine N, Shirakawa T, Minato Y, et al. Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening. PLoS Comput Biol 2009;5:e1000397
63. Michielan L, Bolcato C, Federico S, et al. Combining selectivity and affinity predictions using an integrated Support Vector Machine (SVM) approach: An alternative tool to discriminate between the human adenosine A(2A) and A(3) receptor pyrazolo-triazolo-pyrimidine antagonists binding sites. Bioorg Med Chem 2009;17:5259-74
64. Plewczynski D. Brainstorming: weighted voting prediction of inhibitors for protein targets. J Mol Model 2011;17:2133-41
65. Cheng F, Yu Y, Shen J, et al. Classification of cytochrome p450 inhibitors and noninhibitors using combined classifiers. J Chem Inf Model 2011;51:996-1011
66. Amini A, Muggleton SH, Lodhi H, Sternberg MJE. A novel logic-based approach for quantitative toxicology prediction. J Chem Inf Model 2007;47:998-1006
67. Amini A, Shrimpton PJ, Muggleton SH, Sternberg MJE. A general approach for developing system specific functions to score protein-ligand docked complexes using support vector inductive logic programming. Proteins 2007;69:823-31
68. Cannon EO, Amini A, Bender A, et al. Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. J Comput Aided Mol Des 2007;21:269-80
69. Byvatov E, Schneider G. SVM-based feature selection for characterization of focused compound collections. J Chem Inf Comput Sci 2004;44:993-9
70. Stumpfe D, Bill A, Novak N, et al. Targeting multifunctional proteins by virtual screening: Structurally diverse cytohesin inhibitors with differentiated biological functions. ACS Chem Biol 2010;5:839-49
71. Leong MK. A novel approach using pharmacophore ensemble/support vector machine (PhE/SVM) for prediction of hERG liability. Chem Res Toxicol 2007;20:217-26
72. Chen C-N, Shih Y-H, Ding Y-L, Leong MK. Predicting activation of the promiscuous human pregnane X receptor by pharmacophore ensemble/support vector machine approach. Chem Res Toxicol 2011;24:1765-78
73. Xie Q-Q, Zhong L, Pan Y-L, et al. Combined SVM-based and dockingbased virtual screening for retrieving novel inhibitors of c-Met. Eur J Med Chem 2011;46:3675-80
74. Ren J-X, Li L-L, Zheng R-L, et al. Discovery of novel Pim-1 kinase inhibitors by a hierarchical multistage virtual screening approach based on SVM model, pharmacophore, and molecular docking. J Chem Inf Model 2011;51:1364-75
75. Meslamani J, Bhajun R, Martz F, Rognan D. Computational profiling of bioactive compounds using a targetdependent composite workflow. J Chem Inf Model 2013;53:2322-33