Atom pair 2D-fingerprints perceive 3D-molecular shape and pharmacophores for very fast virtual screening of ZINC and GDB-17

Journal of Chemical Information and Modeling

Volumn 54, Issue 7, 2014, Pages 1892-1907

  Awale, Mahendra ^a   Reymond, Jean Louis ^a  

^a UNIVERSITY OF BERN   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ATOMS; BIOACTIVITY; NEAREST NEIGHBOR SEARCH; PHARMACODYNAMICS; ZINC COMPOUNDS;

ACTIVE COMPOUNDS; NEAREST-NEIGHBOR SEARCHING; ORGANIC MOLECULES; SCORING FUNCTIONS; THREEDIMENSIONAL (3-D); TOPOLOGICAL DISTANCE; VERY LARGE DATABASE; VIRTUAL SCREENING;

VIRTUAL REALITY;

LIGAND;

COMPUTER INTERFACE; CONFORMATION; DRUG DATABASE; DRUG SCREENING; INFORMATION SCIENCE; PROCEDURES;

DATABASES, PHARMACEUTICAL; DRUG EVALUATION, PRECLINICAL; INFORMATICS; LIGANDS; MOLECULAR CONFORMATION; USER-COMPUTER INTERFACE;

EID: 84904994758     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci500232g     Document Type: Article

References (69)

1. Bleicher, K. H.; Bohm, H. J.; Muller, K.; Alanine, A. I. Hit and lead generation: Beyond high-throughput screening Nat. Rev. Drug Discovery 2003, 2, 369-378
2. Renner, S.; Popov, M.; Schuffenhauer, A.; Roth, H. J.; Breitenstein, W.; Marzinzik, A.; Lewis, I.; Krastel, P.; Nigsch, F.; Jenkins, J.; Jacoby, E. Recent trends and observations in the design of high-quality screening collections Future Med. Chem. 2011, 3, 751-766
3. Hann, M. M. Molecular obesity, potency and other addictions in drug discovery MedChemComm 2011, 2, 349-355
4. Sauer, W. H.; Schwarz, M. K. Molecular shape diversity of combinatorial libraries: A prerequisite for broad bioactivity J. Chem. Inf. Comput. Sci. 2003, 43, 987-1003
5. Rush, T. S.; Grant, J. A.; Mosyak, L.; Nicholls, A. A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction J. Med. Chem. 2005, 48, 1489-1495
6. Venhorst, J.; Núnez, S.; Terpstra, J. W.; Kruse, C. G. Assessment of scaffold hopping efficiency by use of molecular interaction fingerprints J. Med. Chem. 2008, 51, 3222-3229
7. 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
8. 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
9. Ebalunode, J. O.; Zheng, W. Molecular shape technologies in drug discovery: Methods and applications Curr. Top. Med. Chem. 2010, 10, 669-679
10. 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
11. Kim, S.; Bolton, E. E.; Bryant, S. H. PubChem3D: Conformer ensemble accuracy J. Cheminform. 2013, 5, 1-17
12. Wirth, M.; Volkamer, A.; Zoete, V.; Rippmann, F.; Michielin, O.; Rarey, M.; Sauer, W. H. Protein pocket and ligand shape comparison and its application in virtual screening J. Comput.-Aided Mol. Des. 2013, 27, 511-524
13. Lovering, F.; Bikker, J.; Humblet, C. Escape from flatland: Increasing saturation as an approach to improving clinical success J. Med. Chem. 2009, 52, 6752-6756
14. Ritchie, T. J.; Macdonald, S. J.; Young, R. J.; Pickett, S. D. The impact of aromatic ring count on compound developability: Further insights by examining carbo- and hetero-aromatic and -aliphatic ring types Drug Discovery Today 2011, 16, 164-171
15. Fink, T.; Bruggesser, H.; Reymond, J. L. Virtual exploration of the small-molecule chemical universe below 160 Da Angew. Chem., Int. Ed. 2005, 44, 1504-1508
16. Fink, T.; Reymond, J. L. Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: Assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery J. Chem. Inf. Model. 2007, 47, 342-353
17. Blum, L. C.; Reymond, J. L. 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13 J. Am. Chem. Soc. 2009, 131, 8732-8733
18. Ruddigkeit, L.; van Deursen, R.; Blum, L. C.; Reymond, J. L. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17 J. Chem. Inf. Model. 2012, 52, 2864-2875
19. Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J. P. ChEMBL: A large-scale bioactivity database for drug discovery Nucleic Acids Res. 2012, 40, D1100-D1107
20. Wang, Y.; Xiao, J.; Suzek, T. O.; Zhang, J.; Wang, J.; Bryant, S. H. PubChem: A public information system for analyzing bioactivities of small molecules Nucleic Acids Res. 2009, 37, W623-W633
21. Irwin, J. J.; Sterling, T.; Mysinger, M. M.; Bolstad, E. S.; Coleman, R. G. ZINC: A free tool to discover chemistry for biology J. Chem. Inf. Model. 2012, 52, 1757-1768
22. Olender, R.; Rosenfeld, R. A fast algorithm for searching for molecules containing a pharmacophore in very large virtual combinatorial libraries J. Chem. Inf. Comput. Sci. 2001, 41, 731-738
23. Klebe, G. Virtual ligand screening: Strategies, perspectives and limitations Drug Discovery Today 2006, 11, 580-594
24. 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
25. Kolb, P.; Ferreira, R. S.; Irwin, J. J.; Shoichet, B. K. Docking and chemoinformatic screens for new ligands and targets Curr. Opin. Biotechnol. 2009, 20, 429-36
26. 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, 205-216
27. Reymond, J. L.; Van Deursen, R.; Blum, L. C.; Ruddigkeit, L. Chemical space as a source for new drugs MedChemComm 2010, 1, 30-38
28. Reymond, J. L.; Awale, M. Exploring chemical space for drug discovery using the chemical universe database ACS Chem. Neurosci. 2012, 3, 649-657
29. Yu, M. J. Druggable chemical space and enumerative combinatorics J. Cheminform. 2013, 5, 19-30
30. Virshup, A. M.; Contreras-Garcia, J.; Wipf, P.; Yang, W.; Beratan, D. N. Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds J. Am. Chem. Soc. 2013, 135, 7296-7303
31. Willett, P. Similarity-based virtual screening using 2D fingerprints Drug Discovery Today 2006, 11, 1046-1053
32. Blum, L. C.; van Deursen, R.; Bertrand, S.; Mayer, M.; Burgi, J. J.; Bertrand, D.; Reymond, J. L. Discovery of alpha7-nicotinic receptor ligands by virtual screening of the chemical universe database GDB-13 J. Chem. Inf. Model. 2011, 51, 3105-3112
33. Burgi, J. J.; Awale, M.; Boss, S. D.; Schaer, T.; Marger, F.; Viveros-Paredes, J. M.; Bertrand, S.; Gertsch, J.; Bertrand, D.; Reymond, J. L. Discovery of potent positive allosteric modulators of the alpha3beta2 nicotinic acetylcholine receptor by a chemical space walk in ChEMBL ACS Chem. Neurosci. 2014, 5, 346-359
34. Nguyen, K. T.; Syed, S.; Urwyler, S.; Bertrand, S.; Bertrand, D.; Reymond, J. L. Discovery of NMDA glycine site inhibitors from the chemical universe database GDB ChemMedChem. 2008, 3, 1520-1524
35. Luethi, E.; Nguyen, K. T.; Burzle, M.; Blum, L. C.; Suzuki, Y.; Hediger, M.; Reymond, J. L. Identification of selective norbornane-type aspartate analogue inhibitors of the glutamate transporter 1 (GLT-1) from the chemical universe generated database (GDB) J. Med. Chem. 2010, 53, 7236-7250
36. Garcia-Delgado, N.; Bertrand, S.; Nguyen, K. T.; van Deursen, R.; Bertrand, D.; Reymond, J.-L. Exploring a7-nicotinic receptor ligand diversity by scaffold enumeration from the chemical universe database GDB ACS Med. Chem. Lett. 2010, 1, 422-426
37. Brethous, L.; Garcia-Delgado, N.; Schwartz, J.; Bertrand, S.; Bertrand, D.; Reymond, J. L. Synthesis and nicotinic receptor activity of chemical space analogues of N -(3 R)-1-azabicyclo[2.2.2]oct-3-yl-4-chlorobenzamide (PNU-282,987) and 1,4-diazabicyclo[3.2.2]nonane-4-carboxylic acid 4-bromophenyl ester (SSR180711) J. Med. Chem. 2012, 55, 4605-4618
38. Huang, N.; Shoichet, B. K.; Irwin, J. J. Benchmarking sets for molecular docking J. Med. Chem. 2006, 49, 6789-6801
39. Ebalunode, J. O.; Zheng, W. Unconventional 2D shape similarity method affords comparable enrichment as a 3D shape method in virtual screening experiments J. Chem. Inf. Model. 2009, 49, 1313-1320
40. Hu, G.; Kuang, G.; Xiao, W.; Li, W.; Liu, G.; Tang, Y. Performance evaluation of 2D fingerprint and 3D shape similarity methods in virtual screening J. Chem. Inf. Model. 2012, 52, 1103-1013
41. Kalaszi, A.; Szisz, D.; Imre, G.; Polgar, T. Screen3D: A novel fully flexible high-throughput shape-similarity search method J. Chem. Inf. Model. 2014, 54, 1036-1049
42. Koutsoukas, A.; Paricharak, S.; Galloway, W. R.; Spring, D. R.; Ijzerman, A. P.; Glen, R. C.; Marcus, D.; Bender, A. How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space J. Chem. Inf. Model. 2014, 54, 230-242
43. Carhart, R. E.; Smith, D. H.; Venkataraghavan, R. Atom pairs as molecular features in structure-activity studies: definition and applications J. Chem. Inf. Comput. Sci. 1985, 25, 64-73
44. Schneider, G.; Neidhart, W.; Giller, T.; Schmid, G. "Scaffold- hopping" by topological pharmacophore search: A contribution to virtual screening Angew. Chem., Int. Ed. 1999, 38, 2894-2896
45. Khalifa, A. A.; Haranczyk, M.; Holliday, J. Comparison of nonbinary similarity coefficients for similarity searching, clustering and compound selection J. Chem. Inf. Model. 2009, 49, 1193-1201
46. Awale, M.; Reymond, J. L. A multi-fingerprint browser for the ZINC database Nucleic Acids Res. 2014, 10.1093/nar/gku379
47. Mavridis, L.; Hudson, B. D.; Ritchie, D. W. Toward high throughput 3D virtual screening using spherical harmonic surface representations J. Chem. Inf. Model. 2007, 47, 1787-1796
48. Brown, R. D.; Martin, Y. C. The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding J. Chem. Inf. Comput. Sci. 1997, 37, 1-9
49. Randic, M. Novel shape descriptors for molecular graphs J. Chem. Inf. Comput. Sci. 2001, 41, 607-613
50. Haigh, J. A.; Pickup, B. T.; Grant, J. A.; Nicholls, A. Small molecule shape-fingerprints J. Chem. Inf. Model. 2005, 45, 673-684
51. Zhang, Q.; Muegge, I. Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: Ranking, voting, and consensus scoring J. Med. Chem. 2006, 49, 1536-1548
52. Firth, N. C.; Brown, N.; Blagg, J. Plane of best fit: A novel method to characterize the three-dimensionality of molecules J. Chem. Inf. Model. 2012, 52, 2516-2525
53. Ballester, P. J.; Richards, W. G. Ultrafast shape recognition to search compound databases for similar molecular shapes J. Comput. Chem. 2007, 28, 1711-1723
54. Schreyer, A. M.; Blundell, T. USRCAT: Real-time ultrafast shape recognition with pharmacophoric constraints J. Cheminform. 2012, 4, 27-39
55. 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
56. Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints J. Chem. Inf. Model. 2010, 50, 742-754
57. Hagadone, T. R. Molecular substructure similarity searching: efficient retrieval in two-dimensional structure databases J. Chem. Inf. Comput. Sci. 1992, 32, 515-521
58. Nguyen, K. T.; Blum, L. C.; van Deursen, R.; Reymond, J.-L. Classification of organic molecules by molecular quantum numbers ChemMedChem. 2009, 4, 1803-1805
59. van Deursen, R.; Blum, L. C.; Reymond, J. L. A searchable map of PubChem J. Chem. Inf. Model. 2010, 50, 1924-1934
60. Schwartz, J.; Awale, M.; Reymond, J. L. SMIfp (SMILES fingerprint) chemical space for virtual screening and visualization of large databases of organic molecules J. Chem. Inf. Model. 2013, 53, 1979-1989
61. 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
62. 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
63. Venkatraman, V.; Perez-Nueno, V. I.; Mavridis, L.; Ritchie, D. W. Comprehensive comparison of ligand-based virtual screening tools against the DUD data set reveals limitations of current 3D methods J. Chem. Inf. Model. 2010, 50, 2079-2093
64. Blum, L. C.; van Deursen, R.; Reymond, J. L. Visualisation and subsets of the chemical universe database GDB-13 for virtual screening J. Comput.-Aided Mol. Des. 2011, 25, 637-647
65. Ruddigkeit, L.; Blum, L. C.; Reymond, J. L. Visualization and virtual screening of the chemical universe database GDB-17 J. Chem. Inf. Model. 2013, 53, 56-65
66. 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
67. Teague, S. J.; Davis, A. M.; Leeson, P. D.; Oprea, T. The design of leadlike combinatorial libraries Angew. Chem., Int. Ed. 1999, 38, 3743-3748
68. Congreve, M.; Carr, R.; Murray, C.; Jhoti, H. A rule of three for fragment-based lead discovery? Drug Discovery Today 2003, 8, 876-877
69. Sadowski, J.; Gasteiger, J. From atoms and bonds to 3-dimensional atomic coordinates-Automatic model builders Chem. Rev. 1993, 93, 2567-2581