Machine learning optimization of cross docking accuracy

Computational Biology and Chemistry

Volumn 62, Issue , 2016, Pages 133-144

  Bjerrum, Esben J ^a  

^a Wildcard Pharmaceutical Consulting   (Denmark)

Author keywords

Autodock Vina; Cross docking; Docking power; Drug discovery; Machine learning optimization; Molecular docking; Scoring function; Smina

Indexed keywords

ARTIFICIAL INTELLIGENCE; FORECASTING; LIGANDS; MOLECULAR MODELING; SUPERVISED LEARNING;

AUTODOCK VINAS; CROSSDOCKING; DRUG DISCOVERY; MOLECULAR DOCKING; SCORING FUNCTIONS; SMINA;

LEARNING SYSTEMS;

LIGAND;

DRUG DESIGN; MACHINE LEARNING; MOLECULAR DOCKING;

DRUG DESIGN; LIGANDS; MACHINE LEARNING; MOLECULAR DOCKING SIMULATION;

EID: 84966429538     PISSN: 14769271     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiolchem.2016.04.005     Document Type: Article

References (51)

1. Q.U. Ain, A. Aleksandrova, F.D. Roessler, and P.J. Ballester Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening WIREs Comput. Mol. Sci. 5 2015 405 424 10.1002/wcms.1225
2. A. Alhossary, S.D. Handoko, Y. Mu, and C.-K. Kwoh Fast, accurate, and reliable molecular docking with QuickVina 2 Bioinformatics 31 2015 2214 2216 10.1093/bioinformatics/btv082
3. C.D. Andersson, E. Thysell, A. Lindström, M. Bylesjö, F. Raubacher, and A. Linusson A multivariate approach to investigate docking parameters' effects on docking performance J. Chem. Inf. Model. 47 2007 1673 1687 10.1021/ci6005596
4. H.M. Ashtawy, and N.R. Mahapatra Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins BMC Bioinf. 16 Suppl. 6 2015 S3 10.1186/1471-2105-16-S6-S3
5. P.J. Ballester, M. Mangold, N.I. Howard, R.L.M. Robinson, C. Abell, J. Blumberger, and J.B.O. Mitchell Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification J. R. Soc. Interface 9 2012 3196 3207 10.1098/rsif.2012.0569
6. M.R. Bauer, T.M. Ibrahim, S.M. Vogel, and F.M. Boeckler Evaluation and optimization of virtual screening workflows with DEKOIS 2.0 - a public library of challenging docking benchmark sets J. Chem. Inf. Model. 53 2013 1447 1462 10.1021/ci400115b
7. T. Cheng, X. Li, Y. Li, Z. Liu, and R. Wang Comparative assessment of scoring functions on a diverse test set J. Chem. Inf. Model. 49 2009 1079 1093 10.1021/ci9000053
8. A.J. Christofferson, and N. Huang How to benchmark methods for structure-based virtual screening of large compound libraries R. Baron, Computational Drug Discovery and Design 2011 Springer Science + Business Media 187 195 10.1007/978-1-61779-465-0-13
9. B. Ding, J. Wang, N. Li, and W. Wang Characterization of small molecule binding. I. Accurate identification of strong inhibitors in virtual screening J. Chem. Inf. Model. 53 2013 114 122 10.1021/ci300508m
10. J.D. Durrant, A.J. Friedman, K.E. Rogers, and J.A. McCammon Comparing neural-network scoring functions and the state of the art: applications to common library screening J. Chem. Inf. Model. 53 2013 1726 1735 10.1021/ci400042y
11. S. Forli, and A.J. Olson A force field with discrete displaceable waters and desolvation entropy for hydrated ligand docking J. Med. Chem. 55 2012 623 638 10.1021/jm2005145
12. D. Fourches, R. Politi, and A. Tropsha Target-specific native/decoy pose classifier improves the accuracy of ligand ranking in the CSAR benchmark J. Chem. Inf. Model. 55 2015 63 71 10.1021/ci500519w
13. R.A. Friesner, J.L. Banks, R.B. Murphy, T.A. Halgren, J.J. Klicic, D.T. Mainz, M.P. Repasky, E.H. Knoll, M. Shelley, J.K. Perry, D.E. Shaw, P. Francis, and P.S. Shenkin Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy J. Med. Chem. 47 2004 1739 1749 10.1021/jm0306430
14. R. Friesner, R. Murphy, M. Repasky, L. Frye, J. Greenwood, T. Halgren, P. Sanschagrin, and D. Mainz Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes J. Med. Chem. 49 2006 6177 6196 10.1021/jm051256o
15. J. Gabel, Jé Desaphy, and D. Rognan Beware of machine learning-based scoring functions - on the danger of developing black boxes J. Chem. Inf. Model. 54 2014 2807 2815 10.1021/ci500406k
16. T.A. Halgren, R.B. Murphy, R.A. Friesner, H.S. Beard, L.L. Frye, W.T. Pollard, and J.L. Banks Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening J. Med. Chem. 47 2004 1750 1759 10.1021/jm030644s
17. S.D. Handoko, X. Ouyang, C.T.T. Su, C.K. Kwoh, and Y.S. Ong QuickVina: accelerating AutoDock Vina using gradient-based heuristics for global optimization IEEE/ACM Trans. Comput. Biol. Bioinf. 9 2012 1266 1272 10.1109/TCBB.2012.82
18. M.J. Hartshorn, M.L. Verdonk, G. Chessari, S.C. Brewerton, W.T.M. Mooij, P.N. Mortenson, and C.W. Murray Diverse, high-quality test set for the validation of protein-ligand docking performance J. Med. Chem. 50 2007 726 741 10.1021/jm061277y
19. R. Hassan, B. Cohanim, O. de Weck, and G. Venter A comparison of particle swarm optimization and the genetic algorithm Proceedings of the 1st AIAA Multidisciplinary Design Optimization Specialist Conference, American Institute of Aeronautics and Astronautics 2005 1 13 10.2514/6.2005-1897
20. L. Hu, M.L. Benson, R.D. Smith, M.G. Lerner, and H.A. Carlson Binding MOAD (Mother of all databases) Proteins 60 2005 333 340 10.1002/prot.20512
21. S.L. Kinnings, N. Liu, P.J. Tonge, R.M. Jackson, L. Xie, and P.E. Bourne A machine learning-based method to improve docking scoring functions and its application to drug repurposing J. Chem. Inf. Model. 51 2011 408 419 10.1021/ci100369f
22. S.L. Kinnings, N. Liu, P.J. Tonge, R.M. Jackson, L. Xie, and P.E. Bourne A machine learning-based method to improve docking scoring functions and its application to drug repurposing J. Chem. Inf. Model. 51 2011 408 419 10.1021/ci100369f
23. D.R. Koes, M.P. Baumgartner, and C.J. Camacho Lessons learned in empirical scoring with smina from the CSAR benchmarking exercise J. Chem. Inf. Model. 53 2013 1893 1904 10.1021/ci300604z
24. Kroshko, D (2013) OpenOpt: Free scientific-engineering software for mathematical modeling and optimization. http://openopt.org (accessed 08.08.15).
25. Landrum G (2015) RDKit: Open-source cheminformatics. http://www.rdkit.org (accessed 27.12.15).
26. L. Li, B. Wang, and S.O. Meroueh Support vector regression scoring of receptor-ligand complexes for rank-ordering and virtual screening of chemical libraries J. Chem. Inf. Model. 51 2011 2132 2138 10.1021/ci200078f
27. Y. Li, Z. Liu, J. Li, L. Han, J. Liu, Z. Zhao, and R. Wang Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set J. Chem. Inf. Model. 54 2014 1700 1716 10.1021/ci500080q
28. Y. Li, L. Han, Z. Liu, and R. Wang Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results J. Chem. Inf. Model. 54 2014 1717 1736 10.1021/ci500081m
29. H. Li, K.-S. Leung, M.-H. Wong, and P.J. Ballester Improving AutoDock Vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets Mol. Info. 34 2015 115 126 10.1002/minf.201400132
30. Y. Liu, L. Zhao, W. Li, D. Zhao, M. Song, and Y. Yang FIPSDock: a new molecular docking technique driven by fully informed swarm optimization algorithm J. Comput. Chem. 34 2012 67 75 10.1002/jcc.23108
31. X.-Y. Meng, H.-X. Zhang, M. Mezei, and M. Cui Molecular docking: a powerful approach for structure-based drug discovery Curr. Comput. Aided Drug Des. 7 2011 146 157 10.2174/157340911795677602
32. M. Mohri, A. Rostamizadeh, and A. Talwalkar Foundations of Machine Learning 2012 MIT Press London, England
33. G.M. Morris, R. Huey, W. Lindstrom, M.F. Sanner, R.K. Belew, D.S. Goodsell, and A.J. Olson AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility J. Comput. Chem. 30 2009 2785 2791 10.1002/jcc.21256
34. N.M. O'Boyle, M. Banck, C.A. James, C. Morley, T. Vandermeersch, and G.R. Hutchison Open babel: an open chemical toolbox J. Cheminform. 3 2011 33 10.1186/1758-2946-3-33
35. A. Oda, N. Yamaotsu, S. Hirono, Y. Watanabe, S. Fukuyoshi, and O. Takahashi Effects of initial settings on computational protein-ligand docking accuracies for several docking programs Mol. Simul. 41 2014 1027 1034 10.1080/08927022.2014.917300
36. D. Plewczynski, M. Łaźniewski, R. Augustyniak, and K. Ginalski Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database J. Comput. Chem. 32 2011 742 755 10.1002/jcc.21643
37. L.L.C. Schrödinger The PyMOL Molecular Graphics System 2010 Version 1.3r1
38. J.-L. Stigliani, V. Bernardes-Génisson, J. Bernadou, and G. Pratviel Cross-docking study on InhA inhibitors: a combination of Autodock Vina and PM6-DH2 simulations to retrieve bio-active conformations Org. Biomol. Chem. 10 2012 6341 6349 10.1039/c2ob25602a
39. O. Trott, and A.J. Olson AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading J. Comput. Chem. 31 2010 455 461 10.1002/jcc.21334
40. A.I.F. Vaz, and L.N. Vicente A particle swarm pattern search method for bound constrained global optimization J. Glob. Optim. 39 2007 197 219 10.1007/s10898-007-9133-5
41. M.L. Verdonk, J.C. Cole, M.J. Hartshorn, C.W. Murray, and R.D. Taylor Improved protein-ligand docking using GOLD Proteins 52 2003 609 623 10.1002/prot.10465
42. M.L. Verdonk, P.N. Mortenson, R.J. Hall, M.J. Hartshorn, and C.W. Murray Protein-ligand docking against non-native protein conformers J. Chem. Inf. Model. 48 2008 2214 2225 10.1021/ci8002254
43. R. Wang, Y. Lu, X. Fang, and S. Wang An Extensive test of 14∼scoring functions using the PDBbind refined set of 800 protein-ligand complexes J. Chem. Inf. Model. 44 2004 2114 2125 10.1021/ci049733j
44. R. Wang, X. Fang, Y. Lu, and S. Wang The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures J. Med. Chem. 47 2004 2977 2980 10.1021/jm030580l
45. R. Wang, X. Fang, Y. Lu, C.-Y. Yang, and S. Wang The PDBbind database: methodologies and updates J. Med. Chem. 48 2005 4111 4119 10.1021/jm048957q
46. W. Wang, W. He, X. Zhou, and X. Chen Optimization of molecular docking scores with support vector rank regression Proteins 81 2013 1386 1398 10.1002/prot.24282
47. G.L. Warren, C.W. Andrews, A.-M. Capelli, B. Clarke, J. LaLonde, M.H. Lambert, M. Lindvall, N. Nevins, S.F. Semus, S. Senger, G. Tedesco, I.D. Wall, J.M. Woolven, C.E. Peishoff, and M.S. Head A critical assessment of docking programs and scoring functions J. Med. Chem. 49 2006 5912 5931 10.1021/jm050362n
48. W. Xu, A.J. Lucke, and D.P. Fairlie Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets J. Mol. Graphics Model. 57 2015 76 88 10.1016/j.jmgm.2015.01.009
49. T. Young, R. Abel, B. Kim, B.J. Berne, and R.A. Friesner Motifs for molecular recognition exploiting hydrophobic enclosure in protein-ligand binding Proc. Natl. Acad. Sci. U. S. A. 104 2007 808 813 10.1073/pnas.0610202104
50. X. Zhang, S.E. Wong, and F.C. Lightstone Message passing interface and multithreading hybrid for parallel molecular docking of large databases on petascale high performance computing machines J. Comput. Chem. 34 2013 915 927 10.1002/jcc.23214
51. D. Zilian, and C.A. Sotriffer SFCscore RF: a random forest-based scoring function for improved affinity prediction of protein-ligand complexes J. Chem. Inf. Model. 53 2013 1923 1933 10.1021/ci400120b