Data-driven medicinal chemistry in the era of big data

Drug Discovery Today

Volumn 19, Issue 7, 2014, Pages 859-868

  Lusher, Scott J ^a,b   McGuire, Ross ^b,c   Van Schaik, René C ^a   Nicholson, C David ^d   De Vlieg, Jacob ^a,b  

^a NETHERLANDS ESCIENCE CENTER   (Netherlands)

^b RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

^c BioAxis Research   (Netherlands)

^d BAYER CROPSCIENCE AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CURRICULUM; DECISION MAKING; DECISION SUPPORT SYSTEM; DRUG DESIGN; DRUG INDUSTRY; DRUG RESEARCH; DRUG SYNTHESIS; EVIDENCE BASED PRACTICE; HUMAN; INFORMATION LITERACY; INFORMATION SYSTEM; KNOWLEDGE MANAGEMENT; MEDICINAL CHEMISTRY; MOBILE APPLICATION; NONHUMAN; NUCLEAR MAGNETIC RESONANCE; PREDICTION; REVIEW; VELOCITY; ANIMAL; DRUG DEVELOPMENT; PROCEDURES; STATISTICS; TRENDS;

ANIMALS; CHEMISTRY, PHARMACEUTICAL; DRUG DISCOVERY; HUMANS; STATISTICS AS TOPIC;

EID: 84903752225     PISSN: 13596446     EISSN: 18785832     Source Type: Journal    
DOI: 10.1016/j.drudis.2013.12.004     Document Type: Review

References (89)

1. J.G. Lombardino, and J.A. Lowe The role of the medicinal chemist in drug discovery - then and now Nat. Rev. Drug Discov. 3 2004 853 862 (Pubitemid 39405946)
2. C.A. Mattmann Computing: a vision for data science Nature 493 2013 473 475
3. M.P. Long, and R.C. Schonfeld Supporting the Changing Research Practices of Chemists 2013 Available at: http://www.sr.ithaka.org/research-publications/ supporting-changing-research-practices-chemists
4. S.J. Lusher et al. A molecular informatics view on best practice in multi-parameter compound optimization Drug Discov. Today 16 2011 555 568
5. C.A. Nicolaou, and N. Brown Multi-objective optimization methods in drug design Drug Discov. Today Technol. 10 2013 427 435
6. C.A. Nicolaou et al. Molecular optimization using computational multi-objective methods Curr. Opin. Drug Discov. Dev. 10 2007 316 324 (Pubitemid 46800225)
7. M.D. Segall Multi-parameter optimization: identifying high quality compounds with a balance of properties Curr. Pharm. Des. 18 2012 1292 1310
8. J.L. Medina-Franco et al. Shifting from the single to the multitarget paradigm in drug discovery Drug Discov. Today 18 2013 495 501
9. T. Slater et al. Beyond data integration Drug Discov. Today 13 2008 584 589
10. F.A. Zoller, and R. Boutellier Design principles for innovative workspaces to increase efficiency in pharmaceutical R&D: lessons learned from the Novartis campus Drug Discov. Today 18 2013 318 322
11. A.T. Plowright et al. Hypothesis driven drug design: improving quality and effectiveness of the design-make-test-analyse cycle Drug Discov. Today 17 2012 56 62
12. A.T. Chadwick, and M.D. Segall Overcoming psychological barriers to good discovery decisions Drug Discov. Today 15 2010 561 569
13. S.J.F. Macdonald, and P.W. Smith Lead optimization in 12 months? True confessions of a chemistry team Drug Discov. Today 6 2001 947 953 (Pubitemid 32799252)
14. P. Murray-Rust Data-driven Science: A Scientist's View. NSF/JISC 2007 Digital Repositories Workshop 2007 Available at: http://www.sis.pitt.edu/ ∼repwkshop/papers/murray.pdf
15. D.C. Swinney, and J. Anthony How were new medicines discovered? Nat. Rev. Drug Discov. 10 2011 507 519
16. D. Hampton et al. The hidden quality gap in discovery Drug Discov. Today 18 2013 506 509
17. I.S. Peers et al. In search of preclinical robustness Nat. Rev. Drug Discov. 11 2012 733 734
18. L.A. Hothorn Biostatistics in nonclinical and preclinical drug development Biometrical J. 47 2005 282 285 (Pubitemid 40977267)
19. D. Lendrem Statistical support to non-clinical Pharm. Stat. 1 2002 71 73
20. Q. Li et al. PubChem as a public resource for drug discovery Drug Discov. Today 15 2010 1052 1057
21. Y. Wang et al. PubChem: a public information system for analyzing bioactivities of small molecules Nucleic Acids Res. 37 2009 W623 W633
22. E.E. Bolton et al. PubChem: integrated platform of small molecules and biological activities Annu. Rep. Comput. Chem. 4 2008 217 241
23. A. Gaulton et al. ChEMBL: a large-scale bioactivity database for drug discovery Nucleic Acids Res. 40 2012 D1100 D1107
24. A.M. Wassermann, and J. Bajorath BindingDB and ChEMBL: online compound databases for drug discovery Expert Opin. Drug Discov. 6 2011 1 5
25. A.J. Williams et al. Towards a gold standard: regarding quality in public domain chemistry databases and approaches to improving the situation Drug Discov. Today 17 2012 685 701
26. N. Fay The role of the informatics framework in early lead discovery Drug Discov. Today 11 2006 1075 1084 (Pubitemid 44792478)
27. E.J. Baede et al. Integrated project views: decision support platform for drug discovery project teams J. Chem. Inf. Model. 52 2012 1438 1449
28. A. Rojnuckarin et al. ArQiologist: an integrated decision support tool for lead optimization J. Chem. Inf. Model. 45 2005 2 9 (Pubitemid 40736950)
29. S.J. Cho et al. ADAAPT: Amgen's data access, analysis, and prediction tools J. Comput. Aided Mol. Des. 20 2006 249 261 (Pubitemid 44317517)
30. T. Sander et al. OSIRIS, an entirely in-house developed drug discovery informatics system J. Chem. Inf. Model. 49 2009 232 246
31. D.K. Agrafiotis et al. Advanced biological and chemical discovery (ABCD): centralizing discovery knowledge in an inherently decentralized world J. Chem. Inf. Model. 47 2007 1999 2014 (Pubitemid 350275069)
32. D. Loughney et al. To measure is to know: an approach to CADD performance metrics Drug Discov. Today 16 2011 548 554
33. C. Johnstone Medicinal chemistry matters - a call for discipline in our discipline Drug Discov. Today 17 2012 538 543
34. M.M. Hann, and G.M. Keserü Finding the sweet spot: the role of nature and nurture in medicinal chemistry Nat. Rev. Drug Discov. 11 2012 355 365
35. G.R. Robb et al. A chemistry wiki to facilitate and enhance compound design in drug discovery Drug Discov. Today 18 2013 141 147
36. P.S. Kutchukian et al. Inside the mind of a medicinal chemist: the role of human bias in compound prioritization during drug discovery PLoS ONE 7 2012 e48476
37. S.D. Roughley, and A.M. Jordan The medicinal chemist's toolbox: an analysis of reactions used in the pursuit of drug candidates J. Med. Chem. 54 2011 3451 3479
38. A.M. Jordan, and S.D. Roughley Drug discovery chemistry: a primer for the non-specialist Drug Discov. Today 14 2009 731 744
39. A. Mayweg et al. ROCK: the Roche medicinal chemistry knowledge application - design, use and impact Drug Discov. Today 16 2011 691 696
40. M. Hohman et al. Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery Drug Discov. Today 14 2009 261 270
41. D.R. Cheshire How well do medicinal chemists learn from experience? Drug Discov. Today 16 2011 817 821
42. A.J. Williams Internet-based tools for communication and collaboration in chemistry Drug Discov. Today 13 2008 502 506
43. T.I. Oprea et al. Chemical information management in drug discovery: optimizing the computational and combinatorial chemistry interfaces J. Mol. Graph. Model. 18 2000 512 524 541
44. M.-L. Lee et al. DEGAS: sharing and tracking target compound ideas with external collaborators J. Chem. Inf. Model. 52 2012 278 284
45. M.D. Brodney et al. Project-focused activity and knowledge tracker: a unified data analysis, collaboration, and workflow tool for medicinal chemistry project teams J. Chem. Inf. Model. 49 2009 2639 2649
46. A. Yasri et al. REALISIS: a medicinal chemistry-oriented reagent selection, library design, and profiling platform J. Chem. Inf. Comput. Sci. 44 2004 2199 2206
47. J. Boström et al. Exploiting personalized information for reagent selection in drug design Drug Discov. Today 16 2011 181 187
48. A.J. Williams et al. Mobile apps for chemistry in the world of drug discovery Drug Discov. Today 16 2011 928 939
49. A.L. Hopkins et al. Ligand efficiency: a useful metric for lead selection Drug Discov. Today 9 2004 430 431 (Pubitemid 38510559)
50. J.W.M. Nissink Simple size-independent measure of ligand efficiency J. Chem. Inf. Model. 49 2009 1617 1622
51. P.D. Leeson, and B. Springthorpe The influence of drug-like concepts on decision-making in medicinal chemistry Nat. Rev. Drug Discov. 6 2007 881 890 (Pubitemid 350042396)
52. J.E. Ladbury et al. Adding calorimetric data to decision making in lead discovery: a hot tip Nat. Rev. Drug Discov. 9 2010 23 27
53. P. Branthwaite The shrinking of the knowledge base - what is the impact of this on the speed and security of drug development? Drug Discov. Today 15 2010 1 2
54. L.J.S. Knutsen Drug discovery management, small is still beautiful: why a number of companies get it wrong Drug Discov. Today 16 2011 476 484
55. M.S. Lajiness et al. Assessment of the consistency of medicinal chemists in reviewing sets of compounds J. Med. Chem. 47 2004 4891 4896 (Pubitemid 39238264)
56. C.A. Lipinski et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings Adv. Drug Deliv. Rev. 46 1997 3 26
57. A.K. Ghose et al. Knowledge-based chemoinformatic approaches to drug discovery Drug Discov. Today 11 2006 1107 1114 (Pubitemid 44803112)
58. C. Lipinski Lead- and drug-like compounds: the rule-of-five revolution Drug Discov. Today Technol. 1 2004 337 341 (Pubitemid 40186335)
59. C.A. Lipinski Drug-like properties and the causes of poor solubility and poor permeability J. Pharmacol. Toxicol. Methods 44 2001 235 249 (Pubitemid 32239479)
60. T. Wunberg et al. Improving the hit-to-lead process: data-driven assessment of drug-like and lead-like screening hits Drug Discov. Today 11 2006 175 180 (Pubitemid 43375969)
61. W.M. Pardridge Blood-brain barrier delivery Drug Discov. Today 12 2007 54 61 (Pubitemid 46014479)
62. H. van de Waterbeemd et al. Estimation of blood-brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors J. Drug Target. 6 1998 151 165 (Pubitemid 29033982)
63. A.M. Aronov et al. Kinase-likeness and kinase-privileged fragments: toward virtual polypharmacology J. Med. Chem. 51 2008 1214 1222 (Pubitemid 351480383)
64. M. Ingo, and I.J. Enyedy Virtual screening for kinase targets Curr. Med. Chem. 11 2004 693 707 (Pubitemid 38380044)
65. H. Weinmann, and R. Metternich Editorial: drug discovery process for kinase inhibitors ChemBioChem 6 2005 455 459 (Pubitemid 40868133)
66. J.F. Lowrie et al. The different strategies for designing GPCR and kinase targeted libraries Comb. Chem. High Throughput Screen. 7 2004 495 510 (Pubitemid 39115322)
67. K.V. Balakin et al. Property-based design of GPCR-targeted library J. Chem. Inf. Comput. Sci. 42 2002 1332 1342 (Pubitemid 35468320)
68. D.G. Sprous et al. QSAR in the pharmaceutical research setting: QSAR models for broad, large problems Curr. Top. Med. Chem. 10 2010 619 637
69. M. Krier, and M.C. Hutter Bioisosteric similarity of molecules based on structural alignment and observed chemical replacements in drugs J. Chem. Inf. Model. 49 2009 1280 1297
70. M. Wagener, and J.P.M. Lommerse The quest for bioisosteric replacements J. Chem. Inf. Model. 46 2006 677 685
71. M. Devereux, and P.L. Popelier In silico techniques for the identification of bioisosteric replacements for drug design Curr. Top. Med. Chem. 10 2010 657 668
72. O. Roche et al. Development of a virtual screening method for identification of frequent hitters in compound libraries J. Med. Chem. 45 2002 137 142 (Pubitemid 34038548)
73. K.H. Bleicher et al. Hit and lead generation: beyond high-throughput screening Nat. Rev. Drug Discov. 2 2003 369 378 (Pubitemid 37361707)
74. J. Kazius et al. Derivation and validation of toxicophores for mutagenicity prediction J. Med. Chem. 48 2005 312 320 (Pubitemid 40105270)
75. D.P. Williams, and D.J. Naisbitt Toxicophores: groups and metabolic routes associated with increased safety risk Curr. Opin. Drug Discov. Dev. 5 2002 104
76. D.A. Price et al. Physicochemical drug properties associated with in vivo toxicological outcomes: a review Expert Opin. Drug Metab. Toxicol. 5 2009 921 931
77. A. Gaulton, and J.P. Overington Role of open chemical data in aiding drug discovery and design Future Med. Chem. 2 2010 903 907
78. S. Motherwell Cheminformatics and crystallography. The Cambridge Structural Database Database 1 2004 129 174
79. J. Kirchmair et al. The Protein Data Bank (PDB), its related services and software tools as key components for in silico guided drug discovery J. Med. Chem. 51 2008 7021 7040
80. P.W. Rose et al. The RCSB Protein Data Bank: new resources for research and education Nucleic Acids Res. 41 2013 D475 D482
81. G. Nicola et al. Public domain databases for medicinal chemistry J. Med. Chem. 55 2012 6987 7002
82. D.B. Judd Open innovation in drug discovery research comes of age Drug Discov. Today 18 2013 315 317
83. Y. Zhou et al. Large-scale annotation of small-molecule libraries using public databases J. Chem. Inf. Model. 47 2007 1386 1394 (Pubitemid 47210042)
84. G.V. Paolini et al. Global mapping of pharmacological space Nat. Biotechnol. 24 2006 805 815 (Pubitemid 44086621)
85. S. Muresan et al. Making every SAR point count: the development of chemistry connect for the large-scale integration of structure and bioactivity data Drug Discov. Today 16 2011 1019 1030
86. T.J. Howe et al. Data reduction and representation in drug discovery Drug Discov. Today 12 2007 45 53 (Pubitemid 46014478)
87. S. Mente, and M. Kuhn The use of the R language for medicinal chemistry applications Curr. Top. Med. Chem. 12 2012 1957 1964
88. T.J. Ritchie et al. The graphical representation of ADME-related molecule properties for medicinal chemists Drug Discov. Today 16 2011 65 72
89. C. Ahlberg Visual exploration of HTS databases: bridging the gap between chemistry and biology Drug Discov. Today 4 1999 370 376 (Pubitemid 29457487)