Enhancing scoring performance of docking-based virtual screening through machine learning

Current Bioinformatics

Volumn 11, Issue 4, 2016, Pages 408-420

  Silva, Cândida G ^a,b   Simões, Carlos J V ^a,c   Carreiras, Pedro ^d   Brito, Rui M M ^a,b  

^a UNIVERSITY OF COIMBRA   (Portugal)

^b UNIVERSITY OF COIMBRA   (Portugal)

^c BSIM2   (Portugal)

^d UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

AutoDock vina; Docking; Machine learning; Scoring function; SVM; Virtual screening

Indexed keywords

BINDING ENERGY; BINDING SITES; E-LEARNING; LEARNING SYSTEMS; LIGANDS;

AUTODOCK VINAS; BINDING-SITES; CLASSIFICATION MODELS; DOCKING; ENERGY; MACHINE-LEARNING; SCORING FUNCTIONS; SCORING PERFORMANCE; SUPPORT VECTORS MACHINE; VIRTUAL SCREENING;

SUPPORT VECTOR MACHINES;

ADENOSINE DEAMINASE; ADENOSYLHOMOCYSTEINASE; CYCLOOXYGENASE 1; GLYCOGEN PHOSPHORYLASE B; PHOSPHODIESTERASE V; RNA DIRECTED DNA POLYMERASE; SIALIDASE;

ARTICLE; BINDING SITE; EVALUATION STUDY; HIGH THROUGHPUT SCREENING; HUMAN; LIGAND BINDING; MEASUREMENT ACCURACY; MEASUREMENT PRECISION; MOLECULAR DOCKING; PREDICTION; PRIORITY JOURNAL; PROTEIN BINDING; PROTEIN INTERACTION; SOFTWARE; SUPPORT VECTOR MACHINE; VIRTUAL REALITY;

EID: 84986915838     PISSN: 15748936     EISSN: 2212392X     Source Type: Journal    
DOI: None     Document Type: Article

References (52)

1. Green DV. Virtual screening of chemical libraries for drug discovery. Expert Opin Drug Discov 2008; 3: 1011-26.
2. Kar S, Roy K. How far can virtual screening take us in drug discovery?. Expert Opin Drug Discov 2013; 8: 245-61.
3. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 2004; 3: 935-49.
4. Huang SY, Grinter SZ, Zou X. Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. Phys Chem Chem Phys 2010; 12: 12899-908.
5. Wang JC, Lin JH. Scoring functions for prediction of proteinligand interactions. Curr Pharm Des 2013; 19: 2174-82.
6. Yuriev E, Ramsland PA. Latest developments in molecular docking: 2010-2011 in review. J Mol Recognit 2013; 26: 215-39.
7. Brooijmans N, Humblet C. Chemical space sampling in virtual screening by different crystal structures. Chem Biol Drug Des 2010; 76: 472-479.
8. Cortright DN, Szallasi A. TRP channels and pain. Curr Pharm Des 2009; 15(15): 1736-49.
9. Brooijmans N, Humblet C. Chemical space sampling by different scoring functions and crystal structures. J Comput Aided Mol Des 2010; 24: 433-47.
10. Avram S, Pacureanu LM, Seclaman E, Bora A, Kurunczi L. PLSDA-Docking Optimized Combined Energetic Terms (PLSDADOCET) protocol: a brief evaluation. J Chem Inf Model 2011; 51: 3169-79.
11. Baum B, Muley L, Smolinski M, Heine A, Hangauer D, Klebe G. Non-additivity of functional group contributions in protein-ligand binding: a comprehensive study by crystallography and isothermal titration calorimetry. J Mol Biol 2010; 397: 1042-54.
12. Muley L, Baum B, Smolinski M, Freindorf M, Heine A, Klebe G, Hangauer DG. Enhancementofhydrophobicinteractions and hydrogen bond strength by cooperativity: synthesis, modeling, and molecular dynamics simulations of a congeneric series of thrombin inhibitors. J Med Chem 2010; 53: 2126-35.
13. Cheng T, Li Q, Zhou Z, Wang Y, Bryant SH. Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J 2012; 14: 133-41.
14. Wang JC, Lin JH. Scoring functions for prediction of proteinligand interactions. Curr Pharm Des 2013; 19: 2174-82.
15. Melville JL, Burke EK, Hirst JD. Machine learning in virtual screening. Comb Chem High Throughput Screen 2009; 12: 332-43.
16. Cannon EO, Amini A, Bender A, Sternberg MJ, Muggleton SH, Glen RC, Mitchell JB. 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.
17. Geppert H, Vogt M, Bajorath J. Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 2010; 50: 205-16.
18. Kinnings SL, Liu N, Tonge PJ, Jackson RM, Xie L, Bourne PE. A machine learning-based method to improve docking scoring functions and its application to drug repurposing. J Chem Inf Model 2011; 51: 408-19.
19. Mitchell JB. Informatics, machine learning and computational medicinal chemistry. Future Med Chem 2011; 3: 451-67.
20. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem 2010; 31: 455-61.
21. Huang N, Shoichet BK, Irwin JJ. Benchmarking Sets for Molecular Docking. J Med Chem 2006; 49: 6789-801.
22. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE. UCSF Chimera-a visualization system for exploratory research and analysis. J Comput Chem 2004; 25: 1605-12.
23. Dunbrack RL Jr, Rotamer libraries in the 21st century. Curr Opin Struct Biol 2002; 12: 431-40.
24. The Open Babel Package, version 2.2.3. http://openbabel.sourceforge.net.
25. Guha R, Howard MT, et al. The Blue Obelisk- interoperability in chemical informatics. J Chem Inf Model 2006; 46: 991-8.
26. Berman HM, Westbrook J, Feng Z, et al. The Protein Data Bank. Nucleic Acids Res 2000; 28: 235-42.
27. Terasaka T, Kinoshita T, Kuno M, Nakanishi I. A highly potent non-nucleoside adenosine deaminase inhibitor: efficient drug discovery by intentional lead hybridization. J Am Chem Soc 2004; 126: 34-5.
28. Gupta K, Selinsky BS, Kaub CJ, Katz AK, Loll PJ. The 2.0 A resolution crystal structure of prostaglandin H2 synthase-1: structural insights into an unusual peroxidase. J Mol Biol 2004; 335: 503-18.
29. Gregoriou M, Noble ME, Watson KA, et al. The structure of a glycogen phosphorylase glucopyranose spirohydantoin complex at 1.8 A resolution and 100 K: the role of the water structure and its contribution to binding. Protein Sci 1998; 7: 915-27.
30. Hopkins AL, Ren J, Esnouf RM, et al. Complexes of HIV-1 reverse transcriptase with inhibitors of the HEPT series reveal conformational changes relevant to the design of potent nonnucleoside inhibitors. J Med Chem 1996; 39: 1589-600.
31. Taylor NR, Cleasby A, Singh O, et al. Dihydropyrancarboxamides related to zanamivir: a new series of inhibitors of influenza virus sialidases. 2. Crystallographic and molecular modeling study of complexes of 4-amino-4H-pyran-6-carboxamides and sialidase from influenza virus types A and B. J Med Chem 1998; 41: 798-807.
32. Card GL, England BP, Suzuki Y, et al. Structural basis for the activity of drugs that inhibit phospho- diesterases. Structure 2004; 12: 2233-47.
33. Turner MA, Yuan CS, Borchardt RT, Hershfield MS, Smith GD, Howell PL. Structure determination of selenomethionyl S adenosylhomocysteine hydrolase using data at a single wavelength. Nat Struct Biol 1998; 5: 369-76.
34. Neudert G, Klebe G. fconv: Format conversion, manipulation and feature computation of molecular data. Bioinformatics 2011; 27: 1021-2.
35. Vapnik VN. The Nature of Statistical Learning Theory. Springer- Verlag: New York 1995.
36. Joachims T. Making Large-Scale SVM Learning Practical. In: Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge, MA, USA; pp. 169-84.
37. Truchon JF, Bayly CI. Evaluating Virtual Screening Methods: Good and Bad Metrics for the “Early Recognition” Problem. J Chem Inf Model 2007; 47: 488508.
38. Jain AN, Nicholls A. Recommendations for evaluation of computational methods. J Comput Aided Mol Des 2008; 22: 133-9.
39. Nicholls A. What do we know and when do we know it?. J Comput Aided Mol Des 2008; 22: 239-55.
40. Zhao W, Hevener KE, White SW, Lee RE, Boyett JM. A statistical framework to evaluate virtual screening. BMC Bioinformatics 2009; 10: 225.
41. Triballeau N, Acher F, Brabet I, Pin JP, Bertrand HO. Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem 2005; 48: 2534-47.
42. Good AC, Oprea TI. Optimization of CAMD techniques 3. Virtual screening enrichment studies: A help or hindrance in tool selection?. J Comput Aided Mol Des 2008; 22: 169-78.
43. Clark RD, Webster-Clark DJ. Managing bias in ROC curves. J Comput Aided Mol Des 2008; 22: 141-6.
44. Bender A, Glen RC. A discussion of measures of enrichment in virtual screening: comparing the information content of descriptors with increasing levels of sophistication. J Chem Inf Model 2005; 45: 1369-75.
45. Yabuuchi H. Package ‘enrichvs’ -Enrichment assessment of virtual screening approaches. The Comprehensive R Archive Network. Reference available from: http://cran.at.rproject.org/web/packages/enrichvs/enrichvs.pdf.
46. Valentini G, Re M. Package ‘PerfMeas’-Performance Measures for ranking and classification tasks. The Comprehensive R Archive Network. Reference available from: http://cran.rproject.org/web/packages/PerfMeas/PerfMeas.pdf.
47. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.
48. Simões CJV. Virtual Screening with Sense and Sensibility in the Search of New Amyloid Inhibitors. PhD dissertation. Coimbra, Portugal: University of Coimbra 2011.
49. Verkhivker GM, Bouzida D, Gehlhaar DK, et al. Deciphering common failures in molecular docking of ligand-protein complexes. J Comput Aided Mol Des 2000; 14: 731-51.
50. Källblad P, Mancera RL, Todorov NP. Assessment of multiple binding modes in ligand-protein docking. J Med Chem 2004; 47: 3334-7.
51. Ivanciuc O. Applications of Support Vector Machines in Chemistry. In: Reviews in Computational Chemistry; Lipkowitz KB and T. R. Cundari, Eds. Wiley-VCH: Weinheim 2007; 23: pp. 291-400.
52. Heikamp K, Bajorath J. Support vector machines for drug discovery. Expert Opin Drug Discov 2014; 9: 93-104.