Application of support vector machine to three-dimensional shape-based virtual screening using comprehensive three-dimensional molecular shape overlay with known inhibitors

Journal of Chemical Information and Modeling

Volumn 52, Issue 4, 2012, Pages 1015-1026

  Sato, Tomohiro ^a   Yuki, Hitomi ^a   Takaya, Daisuke ^a   Sasaki, Shunta ^a   Tanaka, Akiko ^a   Honma, Teruki ^a  

^a RIKEN   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

MOLECULAR STRUCTURE; PREDICTIVE ANALYTICS; PROTEINS; SUPPORT VECTOR MACHINES; VIRTUAL REALITY;

3D SHAPE SIMILARITIES; EXPLANATORY VARIABLES; MOLECULAR SHAPES; PREDICTION PERFORMANCE; SCREENING EFFICIENCY; THREE-DIMENSIONAL SHAPE; THREEDIMENSIONAL (3-D); VIRTUAL SCREENING;

LEARNING SYSTEMS;

ENZYME INHIBITOR; LIGAND; PROTEIN; MOLECULAR LIBRARY; PROTEIN BINDING;

ARTICLE; BINDING SITE; CHEMICAL DATABASE; CHEMICAL PHENOMENA; CHEMISTRY; CONFORMATION; DRUG ANTAGONISM; DRUG DEVELOPMENT; HIGH THROUGHPUT SCREENING; HUMAN; HYDROGEN BOND; MOLECULAR DOCKING; MOLECULAR LIBRARY; PROTEIN BINDING; SUPPORT VECTOR MACHINE; ANTAGONISTS AND INHIBITORS;

BINDING SITES; DATABASES, CHEMICAL; DRUG DISCOVERY; ENZYME INHIBITORS; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; HYDROGEN BONDING; HYDROPHOBIC AND HYDROPHILIC INTERACTIONS; LIGANDS; MOLECULAR CONFORMATION; MOLECULAR DOCKING SIMULATION; PROTEIN BINDING; PROTEINS; SMALL MOLECULE LIBRARIES; SUPPORT VECTOR MACHINES;

EID: 84862018461     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci200562p     Document Type: Article

References (70)

1. MACCS Structural keys; Accelrys: San Diego, CA, 2011.
2. Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints. J. Chem. Inf. Model 2010, 50, 742-754.
3. Willett, P. Similarity-based virtual screening using 2D fingerprints. Drug Discovery Today 2006, 11, 1046-1053. (Pubitemid 44792477)
4. Moffat, K.; Gillet, V. J.; Whittle, M.; Bravi, G.; Leach, A. R. A comparison of field-based similarity searching methods: CatShape, FBSS, and ROCS. J. Chem. Inf. Model. 2008, 48, 719-729. (Pubitemid 351757739)
5. Simoes, C. J.; Mukherjee, T.; Brito, R. M.; Jackson, R. M. Toward the discovery of functional transthyretin amyloid inhibitors: application of virtual screening methods. J. Chem. Inf. Model. 2010, 50, 1806-1820.
6. Kortagere, S.; Krasowski, M. D.; Ekins, S. The importance of discerning shape in molecular pharmacology. Trends Pharmacol. Sci. 2009, 30, 138-147.
7. Nicholls, A.; McGaughey, G. B.; Sheridan, R. P.; Good, A. C.; Warren, G.; Mathieu, M.; Muchmore, S. W.; Brown, S. P.; Grant, J. A.; Haigh, J. A.; Nevins, N.; Jain, A. N.; Kelley, B. Molecular shape and medicinal chemistry: a perspective. J. Med. Chem. 2010, 53, 3862-3886.
8. Ebalunode, J. O.; Zheng, W. Molecular shape technologies in drug discovery: methods and applications. Curr. Top. Med. Chem. 2010, 10, 669-679.
9. Ewing, T. J.; Makino, S.; Skillman, A. G.; Kuntz, I. D. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J. Comput.-Aided. Mol. Des. 2001, 15, 411-428. (Pubitemid 32452109)
10. Goodsell, D. S.; Lauble, H.; Stout, C. D.; Olson, A. J. Automated docking in crystallography: analysis of the substrates of aconitase. Proteins 1993, 17, 1-10. (Pubitemid 23261053)
11. Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K.; Shaw, D. E.; Francis, P.; Shenkin, P. S. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 2004, 47, 1739-1749. (Pubitemid 38380917)
12. Cavasotto, C. N.; Orry, A. J. Ligand docking and structure-based virtual screening in drug discovery. Curr. Top. Med. Chem. 2007, 7, 1006-1014. (Pubitemid 47040528)
13. Giganti, D.; Guillemain, H.; Spadoni, J. L.; Nilges, M.; Zagury, J. F.; Montes, M. Comparative evaluation of 3D virtual ligand screening methods: impact of the molecular alignment on enrichment. J. Chem. Inf. Model. 2010, 50, 992-1004.
14. Hawkins, P. C.; Skillman, A. G.; Nicholls, A. Comparison of shape-matching and docking as virtual screening tools. J. Med. Chem. 2007, 50, 74-82. (Pubitemid 46105500)
15. Sheridan, R. P.; McGaughey, G. B.; Cornell, W. D. Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results. J. Comput.-Aided. Mol. Des. 2008, 22, 257-265.
16. Lemmen, C.; Lengauer, T.; Klebe, G. FLEXS: a method for fast flexible ligand superposition. J. Med. Chem. 1998, 41, 4502-4520. (Pubitemid 28516449)
17. McGaughey, G. B.; Sheridan, R. P.; Bayly, C. I.; Culberson, J. C.; Kreatsoulas, C.; Lindsley, S.; Maiorov, V.; Truchon, J. F.; Cornell, W. D. Comparison of topological, shape, and docking methods in virtual screening. J. Chem. Inf. Model. 2007, 47, 1504-1519. (Pubitemid 47210053)
18. ROCS; OpenEye Scientific Software, Inc., Santa Fe, NM, USA, 2008.
19. Grant, J. A.; Gallardo, M. A.; Pickup, B. T. A fast method of molecular shape comparison: A simple application of a Gaussian description of molecular shape. J. Comput. Chem. 1996, 17, 1653-1666. (Pubitemid 126535414)
20. Dixon, S. L.; Smondyrev, A. M.; Knoll, E. H.; Rao, S. N.; Shaw, D. E.; Friesner, R. A. PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J. Comput.-Aided. Mol. Des. 2006, 20, 647-671. (Pubitemid 46023928)
21. Tresadern, G.; Bemporad, D.; Howe, T. T Acmparison of ligand based virtual screening methods and application to corticotropin releasing factor 1 receptor. J. Mol. Graphics Modell. 2009, 27, 860-870.
22. Naylor, E.; Arredouani, A.; Vasudevan, S. R.; Lewis, A. M.; Parkesh, R.; Mizote, A.; Rosen, D.; Thomas, J. M.; Izumi, M.; Ganesan, A.; Galione, A.; Churchill, G. C. Identification of a chemical probe for NAADP by virtual screening. Nat. Chem. Biol. 2009, 5, 220-226.
23. Freitas, R. F.; Oprea, T. I.; Montanari, C. A. 2D QSAR and similarity studies on cruzain inhibitors aimed at improving selectivity over cathepsin L. Bioorg. Med. Chem. 2008, 16, 838-853. (Pubitemid 351175640)
24. Perez-Nueno, V. I.; Ritchie, D. W.; Rabal, O.; Pascual, R.; Borrell, J. I.; Teixido, J. Comparison of ligand-based and receptorbased virtual screening of HIV entry inhibitors for the CXCR4 and CCR5 receptors using 3D ligand shape matching and ligand-receptor docking. J. Chem. Inf. Model. 2008, 48, 509-533. (Pubitemid 351535420)
25. Bostrom, J.; Berggren, K.; Elebring, T.; Greasley, P. J.; Wilstermann, M. Scaffold hopping, synthesis and structure-activity relationships of 5, 6-diaryl-pyrazine-2-amide derivatives: a novel series of CB1 receptor antagonists. Bioorg. Med. Chem. 2007, 15, 4077-4084. (Pubitemid 46695824)
26. Jacobsson, M.; Garedal, M.; Schultz, J.; Karlen, A. Identification of Plasmodium falciparum spermidine synthase active site binders through structure-based virtual screening. J. Med. Chem. 2008, 51, 2777-2786. (Pubitemid 351620799)
27. Kumar, R. J.; Chebib, M.; Hibbs, D. E.; Kim, H. L.; Johnston, G. A.; Salam, N. K.; Hanrahan, J. R. Novel gamma-aminobutyric acid rho1 receptor antagonists; synthesis, pharmacological activity and structure-activity relationships. J. Med. Chem. 2008, 51, 3825-3840. (Pubitemid 351956513)
28. Hall, M. D.; Salam, N. K.; Hellawell, J. L.; Fales, H. M.; Kensler, C. B.; Ludwig, J. A.; Szakacs, G.; Hibbs, D. E.; Gottesman, M. M. Synthesis, activity, and pharmacophore development for isatin-betathiosemicarbazones with selective activity toward multidrug-resistant cells. J. Med. Chem. 2009, 52, 3191-3204.
29. Broer, B. M.; Gurrath, M.; Holtje, H. D. Molecular modelling studies on the ORL1-receptor and ORL1-agonists. J. Comput.-Aided. Mol. Des. 2003, 17, 739-754. (Pubitemid 38418286)
30. Abate, C.; Mosier, P. D.; Berardi, F.; Glennon, R. A. A structureaffinity and comparative molecular field analysis of sigma-2 (sigma2) receptor ligands. Cent. Nerv. Syst. Agents Med. Chem. 2009, 9, 246-257.
31. Schneider, G.; Wrede, P. Artificial neural networks for computer-based molecular design. Prog. Biophys. Mol. Biol. 1998, 70, 175-222. (Pubitemid 28487613)
32. Agrafiotis, D. K.; Cedeno, W.; Lobanov, V. S. On the use of neural network ensembles in QSAR and QSPR. J. Chem. Inf. Comput. Sci. 2002, 42, 903-911. (Pubitemid 35355240)
33. Kauffman, G. W.; Jurs, P. C. QSAR and k-nearest neighbor classification analysis of selective cyclooxygenase-2 inhibitors using topologically-based numerical descriptors. J. Chem. Inf. Comput. Sci. 2001, 41, 1553-1560.
34. Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J. C.; Sheridan, R. P.; Feuston, B. P. Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 2003, 43, 1947-1958.
35. 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, 1882-1889.
36. Winkler, D. A. Neural networks as robust tools in drug lead discovery and development. Mol. Biotechnol. 2004, 27, 139-168.
37. Guha, R.; Jurs, P. C. Interpreting computational neural network QSAR models: a measure of descriptor importance. J. Chem. Inf. Model. 2005, 45, 800-806. (Pubitemid 40795183)
38. Plewczynski, D.; Spieser, S. A.; Koch, U. Assessing different classification methods for virtual screening. J. Chem. Inf. Model. 2006, 46, 1098-1106. (Pubitemid 43999155)
39. Chen, B.; Harrison, R. F.; Papadatos, G.; Willett, P.; Wood, D. J.; Lewell, X. Q.; Greenidge, P.; Stiefl, N. Evaluation of machinelearning methods for ligand-based virtual screening. J. Comput.-Aided. Mol. Des. 2007, 21, 53-62. (Pubitemid 46416666)
40. Ehrman, T. M.; Barlow, D. J.; Hylands, P. J. Virtual screening of Chinese herbs with Random Forest. J. Chem. Inf. Model. 2007, 47, 264-278. (Pubitemid 46615932)
41. 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, 1152-1160.
42. Fukunishi, Y. Structure-based drug screening and ligand-based drug screening with machine learning. Comb. Chem. High Throughput Screen. 2009, 12, 397-408.
43. Sato, T.; Honma, T.; Yokoyama, S. Combining machine learning and pharmacophore-based interaction fingerprint for in silico screening. J. Chem. Inf. Model. 2010, 50, 170-185.
44. Kinnings, S. L.; Liu, N.; Tonge, P. J.; Jackson, R. M.; Xie, L.; Bourne, P. E. A machine learning-based method to improve docking scoring functions and its application to drug repurposing. J. Chem. Inf. Model. 2011, 51, 408-419.
45. Yabuuchi, H.; Niijima, S.; Takematsu, H.; Ida, T.; Hirokawa, T.; Hara, T.; Ogawa, T.; Minowa, Y.; Tsujimoto, G.; Okuno, Y. Analysis of multiple compound-protein interactions reveals novel bioactive molecules. Mol. Syst. Biol. 2011, 7, 472.
46. Vapnik, V. The nature of statistical learning theory; Springer-Verlag: New York, 1995.
47. Breiman, L. Random forests. Machine Learn. 2001, 45, 5-32. (Pubitemid 32933532)
48. Whitehead, C. E.; Breneman, C. M.; Sukumar, N.; Ryan, M. D. Transferable atom equivalent multicentered multipole expansion method. J. Comput. Chem. 2003, 24, 512-529.
49. Breneman, C. M.; Sundling, C. M.; Sukumar, N.; Shen, L.; Katt, W. P.; Embrechts, M. J. New developments in PEST shape/property hybrid descriptors. J. Comput.-Aided. Mol. Des. 2003, 17, 231-240. (Pubitemid 37062784)
50. Chekmarev, D. S.; Kholodovych, V.; Balakin, K. V.; Ivanenkov, Y.; Ekins, S.; Welsh, W. J. Shape signatures: new descriptors for predicting cardiotoxicity in silico. Chem. Res. Toxicol. 2008, 21, 1304-1314. (Pubitemid 351967396)
51. Chekmarev, D.; Kholodovych, V.; Kortagere, S.; Welsh, W. J.; Ekins, S. Predicting inhibitors of acetylcholinesterase by regression and classification machine learning approaches with combinations of molecular descriptors. Pharm. Res. 2009, 26, 2216-2224.
52. Haigh, J. A.; Pickup, B. T.; Grant, J. A.; Nicholls, A. Small molecule shape-fingerprints. J. Chem. Inf. Model. 2005, 45, 673-684. (Pubitemid 40795171)
53. Scholkopf, B.; Mika, S.; Burges, C. C.; Knirsch, P.; Muller, K. R.; Ratsch, G.; Smola, A. J. Input space versus feature space in kernelbased methods. IEEE Trans. Neural Netw. 1999, 10, 1000-1017.
54. Tsuda, K. Support vector classifier with asymmetric kernel function. In Proceedings ESANN; Verleysen, M., Ed.; Brussels, 1999; pp 183-188.
55. Scholkopf, B. Smola, A. J. Learning with Kernels; MIT Press: Cambridge, MA, 2002.
56. Warr, W. A. ChEMBL An interview with John Overington, team leader, chemogenomics at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory (EMBL-EBI). J. Comput.-Aided. Mol. Des. 2009, 23, 195-198.
57. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 1997, 23, 3-25. (Pubitemid 27046991)
58. Lajiness, M. S. Molecular similarity-based methods for selecting compounds for screening; In Computational chemical graph theory; Rouvray, D. H.; Nova Science Publishers, Inc.: Commack, NY, USA, 1990; pp 299-316.
59. Bawden, D. 1993. Molecular dissimilarity in chemical information systems, In Chemical Structures 2. The International Language of Chemistry; Warr, W. A., Ed.; Springer-Verlag, Heidelberg, 1993; pp 383-388.
60. Willett, P. Dissimilarity-based algorithms for selecting structurally diverse sets of compounds. J. Comput. Biol. 1999, 6, 447-457.
61. Irwin, J. J.; Shoichet, B. K. ZINC-a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 2005, 45, 177-182. (Pubitemid 40736970)
62. Bostrom, J.; Greenwood, J. R.; Gottfries, J. Assessing the performance of OMEGA with respect to retrieving bioactive conformations. J. Mol. Graphics Modell. 2003, 21, 449-462. (Pubitemid 36120775)
63. Halgren, T. A.; Murphy, R. B.; Friesner, R. A.; Beard, H. S.; Frye, L. L.; Pollard, W. T.; Banks, J. L. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 2004, 47, 1750-1759. (Pubitemid 38380918)
64. Triballeau, N.; Acher, F.; Brabet, I.; Pin, J. P.; Bertrand, H. O. Virtual screening workflow development guided by the ?receiver operating characteristic? curve approach. Application to highthroughput docking on metabotropic glutamate receptor subtype 4. J. Med. Chem. 2005, 48, 2534-2547. (Pubitemid 40520517)
65. Pipeline Pilot; Accelrys Software Inc.: San Diego California, USA, 2007.
66. Sato, T.; Matsuo, Y.; Honma, T.; Yokoyama, S. In silico functional profiling of small molecules and its applications. J. Med. Chem. 2008, 51, 7705-7716.
67. Dayam, R.; Aiello, F.; Deng, J.; Wu, Y.; Garofalo, A.; Chen, X.; Neamati, N. Discovery of small molecule integrin alphavbeta3 antagonists as novel anticancer agents. J. Med. Chem. 2006, 49, 4526-4534. (Pubitemid 44162679)
68. Perez-Nueno, V. I.; Ritchie, D. W.; Borrell, J. I.; Teixido, J. Clustering and classifying diverse HIV entry inhibitors using a novel consensus shape-based virtual screening approach: further evidence for multiple binding sites within the CCR5 extracellular pocket. J. Chem. Inf. Model. 2008, 48, 2146-2165.
69. Kirchmair, J.; Distinto, S.; Markt, P.; Schuster, D.; Spitzer, G. M.; Liedl, K. R.; Wolber, G. How to optimize shape-based virtual screening: choosing the right query and including chemical information. J. Chem. Inf. Model. 2009, 49, 678-692.
70. Perez-Nueno, V. I.; Ritchie, D. W. Using Consensus-Shape Clustering To Identify Promiscuous Ligands and Protein Targets and To Choose the Right Query for Shape-Based Virtual Screening. J. Chem. Inf. Model. 2011, 51, 1233-1248.