Toward a benchmarking data set able to evaluate ligand- and structure-based virtual screening using public HTS data

Journal of Chemical Information and Modeling

Volumn 55, Issue 2, 2015, Pages 343-353

  Lindh, Martin ^a   Svensson, Fredrik ^a   Schaal, Wesley ^a   Zhang, Jin ^a   Sköld, Christian ^a   Brandt, Peter ^a   Karlén, Anders ^a  

^a UPPSALA UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

DRUG PRODUCTS; LIGANDS;

3D SHAPE SIMILARITIES; ACTIVE COMPOUNDS; BENCHMARKING DATA; HIGH THROUGHPUT SCREENING; INACTIVE COMPOUNDS; METHOD EVALUATION; SCREENING PERFORMANCE; VIRTUAL SCREENING;

CRYSTAL STRUCTURE;

LIGAND;

ALGORITHM; BENCHMARKING; CHEMICAL DATABASE; COMPUTER INTERFACE; COMPUTER SIMULATION; CONFORMATION; HIGH THROUGHPUT SCREENING; PROCEDURES; REPRODUCIBILITY; STRUCTURE ACTIVITY RELATION;

ALGORITHMS; BENCHMARKING; COMPUTER SIMULATION; DATABASES, CHEMICAL; HIGH-THROUGHPUT SCREENING ASSAYS; LIGANDS; MOLECULAR CONFORMATION; REPRODUCIBILITY OF RESULTS; STRUCTURE-ACTIVITY RELATIONSHIP; USER-COMPUTER INTERFACE;

EID: 84923359031     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci5005465     Document Type: Article

References (63)

1. Lill, M. Virtual Screening in Drug Design Methods Mol. Biol. (Clifton, N.J.) 2013, 993, 1-12
2. Huang, S.-Y.; Grinter, S. Z.; Zou, X. Scoring Functions and Their Evaluation Methods for Protein-Ligand Docking: Recent Advances and Future Directions Phys. Chem. Chem. Phys. 2010, 12, 12899-12908
3. Svensson, F.; Karlén, A.; Sköld, C. Virtual Screening Data Fusion Using Both Structure- and Ligand-Based Methods J. Chem. Inf. Model. 2011, 52, 225-232
4. Sastry, G. M.; Inakollu, V. S. S.; Sherman, W. Boosting Virtual Screening Enrichments with Data Fusion: Coalescing Hits from Two-Dimensional Fingerprints, Shape, and Docking J. Chem. Inf. Model. 2013, 53, 1531-1542
5. Drwal, M. N.; Griffith, R. Combination of Ligand- and Structure-Based Methods in Virtual Screening Drug Discovery Today Technol. 2013, 10, e395-e401
6. Verdonk, M. L.; Berdini, V.; Hartshorn, M. J.; Mooij, W. T. M.; Murray, C. W.; Taylor, R. D.; Watson, P. Virtual Screening Using Protein-Ligand Docking: Avoiding Artificial Enrichment J. Chem. Inf. Comput. Sci. 2004, 44, 793-806
7. Good, A. C.; Hermsmeier, M. A.; Hindle, S. A. Measuring CAMD Technique Performance: A Virtual Screening Case Study in the Design of Validation Experiments J. Comput. Aided. Mol. Des. 2004, 18, 529-536
8. Bender, A.; Glen, R. C. 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, 1369-1375
9. Huang, N.; Shoichet, B. K.; Irwin, J. J. Benchmarking Sets for Molecular Docking J. Med. Chem. 2006, 49, 6789-6801
10. Mysinger, M. M.; Carchia, M.; Irwin, J. J.; Shoichet, B. K. Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking J. Med. Chem. 2012, 55, 6582-6594
11. Vogel, S. M.; Bauer, M. R.; Boeckler, F. M. DEKOIS: Demanding Evaluation Kits for Objective in Silico Screening - a Versatile Tool for Benchmarking Docking Programs and Scoring Functions J. Chem. Inf. Model. 2011, 51, 2650-2665
12. Rohrer, S. G.; Baumann, K. Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data J. Chem. Inf. Model. 2009, 49, 169-184
13. Irwin, J. Community Benchmarks for Virtual Screening J. Comput. Aided. Mol. Des. 2008, 22, 193-199
14. Cheeseright, T. J.; Mackey, M. D.; Melville, J. L.; Vinter, J. G. FieldScreen: Virtual Screening Using Molecular Fields. Application to the DUD Data Set J. Chem. Inf. Model. 2008, 48, 2108-2117
15. Good, A. C.; Oprea, T. I. Optimization of CAMD Techniques 3. Virtual Screening Enrichment Studies: A Help or Hindrance in Tool Selection? J. Comput. Aided. Mol. Des. 2008, 22, 169-178
16. Bauman, J. D.; Patel, D.; Dharia, C.; Fromer, M. W.; Ahmed, S.; Frenkel, Y.; Vijayan, R. S. K.; Eck, J. T.; Ho, W. C.; Das, K.; Shatkin, A. J.; Arnold, E. Detecting Allosteric Sites of HIV-1 Reverse Transcriptase by X-Ray Crystallographic Fragment Screening J. Med. Chem. 2013, 56, 2738-2746
17. Schierz, A. C. Virtual Screening of Bioassay Data J. Cheminform. 2009, 1, 21
18. Butkiewicz, M.; Lowe, E. W.; Mueller, R.; Mendenhall, J. L.; Teixeira, P. L.; Weaver, C. D.; Meiler, J. Benchmarking Ligand-Based Virtual High-Throughput Screening with the PubChem Database Molecules 2013, 18, 735-756
19. Wang, Y.; Xiao, J.; Suzek, T. O.; Zhang, J.; Wang, J.; Zhou, Z.; Han, L.; Karapetyan, K.; Dracheva, S.; Shoemaker, B. A.; Bolton, E.; Gindulyte, A.; Bryant, S. H. PubChems BioAssay Database Nucleic Acids Res. 2012, 40, D400-12
20. Bernstein, F. C.; Koetzle, T. F.; Williams, G. J.; Meyer, E. F.; Brice, M. D.; Rodgers, J. R.; Kennard, O.; Shimanouchi, T.; Tasumi, M. The Protein Data Bank: A Computer-Based Archival File for Macromolecular Structures J. Mol. Biol. 1977, 112, 535-542
21. Kleywegt, G. J.; Harris, M. R.; Zou, J. Y.; Taylor, T. C.; Wählby, A.; Jones, T. A. The Uppsala Electron-Density Server Acta Crystallogr. 2004, 60, 2240-2249
22. Petrova, T.; Chuprina, A.; Parkesh, R.; Pushechnikov, A. Structural Enrichment of HTS Compounds from Available Commercial Libraries Med. Chem. Commun. 2012, 3, 571
23. Instant JChem. 6.1; ChemAxon: Budapest, Hungary, 2013.
24. Baell, J. B.; Holloway, G. A. New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays J. Med. Chem. 2010, 53, 2719-2740
25. Mok, N. Y.; Maxe, S.; Brenk, R. Locating Sweet Spots for Screening Hits and Evaluating Pan-Assay Interference Filters from the Performance Analysis of Two Lead-like Libraries J. Chem. Inf. Model. 2013, 534-544
26. Canvas, version 1.5; Schrödinger, LLC: New York, 2012.
27. Miller, K. J. Additivity Methods in Molecular Polarizability J. Am. Chem. Soc. 1990, 112, 8533-8542
28. McCabe, P.; Korb, O.; Cole, J. Kernel Density Estimation Applied to Bond Length, Bond Angle, and Torsion Angle Distributions J. Chem. Inf. Model. 2014, 54, 1284-1288
29. Jones, E.; Oliphant, T.; Peterson, P. SciPy: Open Source Scientific Tools for Python.
30. Duan, J.; Dixon, S. L.; Lowrie, J. F.; Sherman, W. Analysis and Comparison of 2D Fingerprints: Insights into Database Screening Performance Using Eight Fingerprint Methods J. Mol. Graphics Model. 2010, 29, 157-170
31. Bemis, G. W.; Murcko, M. A. The Properties of Known Drugs. 1. Molecular Frameworks J. Med. Chem. 1996, 39, 2887-2893
32. LigPrep, version 2.5; Schrödinger, LCC: New York, 2012.
33. Epik, version 2.3; Schrödinger, LCC: New York, 2012.
34. Shelley, J.; Cholleti, A.; Frye, L.; Greenwood, J.; Timlin, M.; Uchimaya, M. Epik: A Software Program for pK a Prediction and Protonation State Generation for Drug-like Molecules J. Comput. Aided. Mol. Des. 2007, 21, 681-691
35. Greenwood, J.; Calkins, D.; Sullivan, A.; Shelley, J. Towards the Comprehensive, Rapid, and Accurate Prediction of the Favorable Tautomeric States of Drug-like Molecules in Aqueous Solution J. Comput. Aided. Mol. Des. 2010, 24, 591-604
36. Protein Preparation Wizzard; Schrödinger, LLC: New York, 2012.
37. Olsson, M. H. M.; Søndergaard, C. R.; Rostkowski, M.; Jensen, J. H. PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical P K a Predictions J. Chem. Theory Comput. 2011, 7, 525-537
38. Glide, version 5.8; Schrödinger, LCC: New York, 2013.
39. 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
40. 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
41. Friesner, R. A.; Murphy, R. B.; Repasky, M. P.; Frye, L. L.; Greenwood, J. R.; Halgren, T. A.; Sanschagrin, P. C.; Mainz, D. T. Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein-Ligand Complexes J. Med. Chem. 2006, 49, 6177-6196
42. ROCS, version 3.1; OpenEye Scientific Software: Santa Fe, NM, 2011.
43. Hawkins, P. C. D.; Skillman, A. G.; Nicholls, A. Comparison of Shape-Matching and Docking as Virtual Screening Tools J. Med. Chem. 2007, 50, 74-82
44. OMEGA, version 2.5; OpenEye Scientific Software: Santa Fe, NM, 2012.
45. Hawkins, P. C. D.; Skillman, A. G.; Warren, G. L.; Ellingson, B. A.; Stahl, M. T. Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database J. Chem. Inf. Model. 2010, 50, 572-584
46. Truchon, J.-F.; Bayly, C. I. Evaluating Virtual Screening Methods: Good and Bad Metrics for the "Early Recognition" Problem J. Chem. Inf. Model. 2007, 47, 488-508
47. Barta, T. E.; Becker, D. P.; Bedell, L. J.; Easton, A. M.; Hockerman, S. L.; Kiefer, J.; Munie, G. E.; Mathis, K. J.; Li, M. H.; Rico, J. G.; Villamil, C. I.; Williams, J. M. MMP-13 Selective A-Sulfone Hydroxamates: A Survey of P1 Heterocyclic Amide Isosteres Bioorg. Med. Chem. Lett. 2011, 21, 2820-2822
48. Wu, S.; Vossius, S.; Rahmouni, S.; Miletic, A. V.; Vang, T.; Vazquez-Rodriguez, J.; Cerignoli, F.; Arimura, Y.; Williams, S.; Hayes, T.; Moutschen, M.; Vasile, S.; Pellecchia, M.; Mustelin, T.; Tautz, L. Multidentate Small-Molecule Inhibitors of Vaccinia H1-Related (VHR) Phosphatase Decrease Proliferation of Cervix Cancer Cells J. Med. Chem. 2009, 52, 6716-6723
49. He, Y.; Liu, S.; Menon, A.; Stanford, S.; Oppong, E.; Gunawan, A. M.; Wu, L.; Wu, D. J.; Barrios, A. M.; Bottini, N.; Cato, A. C. B.; Zhang, Z.-Y. A Potent and Selective Small-Molecule Inhibitor for the Lymphoid-Specific Tyrosine Phosphatase (LYP), a Target Associated with Autoimmune Diseases J. Med. Chem. 2013, 56, 4990-5008
50. Eldrup, A. B.; Soleymanzadeh, F.; Taylor, S. J.; Muegge, I.; Farrow, N. A.; Joseph, D.; McKellop, K.; Man, C. C.; Kukulka, A.; De Lombaert, S. Structure-Based Optimization of Arylamides as Inhibitors of Soluble Epoxide Hydrolase J. Med. Chem. 2009, 52, 5880-5895
51. Zhang, M.; Cho, E.; Burstein, G. Selective Inactivation of a Human Neuronal Silencing Phosphatase by a Small Molecule Inhibitor ACS Chem. Biol. 2011, 511-519
52. Probst, G. D.; Bowers, S.; Sealy, J. M.; Truong, A. P.; Hom, R. K.; Galemmo, R. a; Konradi, A. W.; Sham, H. L.; Quincy, D. a; Pan, H.; Yao, N.; Lin, M.; Tóth, G.; Artis, D. R.; Zmolek, W.; Wong, K.; Qin, A.; Lorentzen, C.; Nakamura, D. F.; Quinn, K. P.; Sauer, J.-M.; Powell, K.; Ruslim, L.; Wright, S.; Chereau, D.; Ren, Z.; Anderson, J. P.; Bard, F.; Yednock, T. a; Griswold-Prenner, I. Highly Selective c-Jun N-Terminal Kinase (JNK) 2 and 3 Inhibitors with in Vitro CNS-like Pharmacokinetic Properties Prevent Neurodegeneration Bioorg. Med. Chem. Lett. 2011, 21, 315-319
53. Malmström, J.; Viklund, J.; Slivo, C.; Costa, A.; Maudet, M.; Sandelin, C.; Hiller, G.; Olsson, L.-L.; Aagaard, A.; Geschwindner, S.; Xue, Y.; Vasänge, M. Synthesis and Structure-Activity Relationship of 4-(1,3-Benzothiazol-2-Yl)-Thiophene-2-Sulfonamides as Cyclin-Dependent Kinase 5 (cdk5)/p25 Inhibitors Bioorg. Med. Chem. Lett. 2012, 22, 5919-5923
54. Blucher, A. S.; McWeeney, S. K. Challenges in Secondary Analysis of High Throughput Screening Data Pac. Symp. Biocomput. 2014, 114-124
55. Shoichet, B. K. Interpreting Steep Dose-Response Curves in Early Inhibitor Discovery J. Med. Chem. 2006, 49, 7274-7277
56. Arkin, M. R.; Wells, J. A. Small-Molecule Inhibitors of Protein-Protein Interactions: Progressing towards the Dream Nat. Rev. Drug Discovery 2004, 3, 301-317
57. García-Sosa, A. T.; Hetényi, C.; Maran, U. Drug Efficiency Indices for Improvement of Molecular Docking Scoring Functions J. Comput. Chem. 2010, 31, 174-184
58. Bender, A.; Mussa, H. Y.; Glen, R. C.; Reiling, S. Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D): Evaluation of Performance J. Chem. Inf. Comput. Sci. 2004, 44, 1708-1718
59. Bender, A.; Mussa, H. Y.; Glen, R. C.; Reiling, S. Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier J. Chem. Inf. Comput. Sci. 2003, 44, 170-178
60. 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
61. Von Korff, M.; Freyss, J.; Sander, T. Comparison of Ligand- and Structure-Based Virtual Screening on the DUD Data Set J. Chem. Inf. Model. 2009, 49, 209-231
62. Zhang, Y.; Yang, S.; Jiao, Y.; Liu, H.; Yuan, H.; Lu, S.; Ran, T.; Yao, S.; Ke, Z.; Xu, J.; Xiong, X.; Chen, Y.; Lu, T. An Integrated Virtual Screening Approach for VEGFR-2 Inhibitors J. Chem. Inf. Model. 2013, 53, 3163-3177
63. Kuglstatter, A.; Wong, A.; Tsing, S.; Lee, S. W.; Lou, Y.; Villaseor, A. G.; Bradshaw, J. M.; Shaw, D.; Barnett, J. W.; Browner, M. F. Insights into the Conformational Flexibility of Brutons Tyrosine Kinase from Multiple Ligand Complex Structures Protein Sci. 2011, 20, 428-436