Entering the 'big data' era in medicinal chemistry: Molecular promiscuity analysis revisited

Future Science OA

Volumn 3, Issue 2, 2017, Pages

  Hu, Ye ^a   Bajorath, Jürgen ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

Big data concept; Big data criteria; Compound activity data; Computational mining; Drug discovery; Medicinal chemistry; Polypharmacology; Promiscuity

Indexed keywords

ACEPROMETAZINE; AMITRIPTYLINE; AMOXAPINE; ANILERIDINE; ANTIARRHYTHMIC AGENT; ANTIHISTAMINIC AGENT; ANTINEOPLASTIC AGENT; CETIRIZINE; CHLOROPYRAMINE; CLONIDINE; CLOZAPINE; DIPYRIDAMOLE; EMTRICITABINE; ESMOLOL; G PROTEIN COUPLED RECEPTOR; IMATINIB; INDECAINIDE; LOFEXIDINE; NABUMETONE; NEUROLEPTIC AGENT; OLANZAPINE; PROPIOMAZINE; PROTEIN TYROSINE KINASE INHIBITOR; RISPERIDONE; SORAFENIB; TERCONAZOLE; TOPOTECAN;

BIOLOGICAL ACTIVITY; COMPUTATIONAL FLUID DYNAMICS; DATA MINING; IC50; INFORMATION PROCESSING; MATCHED MOLECULAR PAIR; MEDICINAL CHEMISTRY; MOLECULAR BIOLOGY; NONHUMAN; POLYPHARMACOLOGY; PRIORITY JOURNAL; PROTEIN POLYMORPHISM; REVIEW; STRUCTURE ACTIVITY RELATION;

EID: 85030694245     PISSN: None     EISSN: 20565623     Source Type: Journal    
DOI: 10.4155/fsoa-2017-0001     Document Type: Review

References (55)

1. Mayer-Schönberger V, Cukier K. Big data: A Revolution That Will Transform How We Live, Work, And Think. Houghton Mifflin Harcourt, MA, USA (2013).
2. 3D data management: controlling data volume, velocity, and variety. https://blogs.gartner.com
3. Gartner. Big data. www.gartner.com/it-glossary/big-data/
4. Datafloq. Why the 3Vs are not sufficient to describe big data. https://datafloq.com
5. Lynch C. Big data: how do your data grow? Nature 455(7209), 28-29 (2008).
6. Marx V. The big challenges of big data. Nature 498(7453), 255-260 (2013).
7. Al-Lazikani B, Workman P. Minimizing bias in target selection by exploiting multidisciplinary Big Data and the protein interactome. Future Med. Chem. 8(14), 1711-1716 (2016).
8. Bajorath J, Jenkins J, Overington J, Walters WP. Drug discovery and development in the era of big data. Future Med. Chem. 8(15), 1807-1813 (2016).
9. Hu Y, Bajorath J. Learning from 'big data': compounds and targets. Drug Discov. Today 19(4), 357-360 (2014).
10. Lusher SJ, McGuire R, van Schaik RC, Nicholson CD, de Vlieg J. Data-driven medicinal chemistry in the era of big data. Drug Discovery Today 19(7), 859-868 (2014).
11. Paolini GV, Shapland RHB, van Hoorn WP, Mason JS, Hopkins AL. Global mapping of pharmacological space. Nat. Biotechnol. 24(7), 805-815 (2006).
12. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4(11), 682-690 (2008).
13. Boran AD, Iyengar R. Systems approaches to polypharmacology and drug discovery. Curr. Opin. Drug Discov. Devel. 13(3), 297-309 (2010).
14. Jalencas X, Mestres J. On the origins of drug polypharmacology. Med. Chem. Comm. 4(1), 80-87 (2013).
15. Anighoro A, Bajorath J, Rastelli G. Polypharmacology: challenges and opportunities in drug discovery: miniperspective. J. Med. Chem. 57(19), 7874-7887 (2014).
16. Hu Y, Bajorath J. Compound promiscuity - what can we learn from current data. Drug Discov. Today 18(13-14), 644-650 (2013).
17. McGovern SL, Caselli E, Grigorieff NA. Common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J. Med. Chem. 45(8), 1712-1722 (1996).
18. Shoichet BK. Screening in a spirit haunted world. Drug Discov. Today 11(13-14), 607-615 (2006).
19. Baell JB, Holloway GA. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J. Med. Chem. 53(7), 2719-2740 (2010).
20. Baell J, Walters MA. Chemistry: chemical con artists foil drug discovery. Nature 513(7519), 481-483 (2014).
21. Hu Y, Bajorath J. Promiscuity profiles of bioactive compounds: potency range and difference distributions and the relation to target numbers and families. Med. Chem. Commun. 4, 1196-1201 (2013).
22. Schneider G, Neidhart W, Giller T, Schmid G. “Scaffold-hopping” by topological pharmacophore search: a contribution to virtual screening. Angew. Chem. Int. Ed. Engl. 38(19), 2894-2896 (1999).
23. Müller G. Medicinal chemistry of target family-directed masterkeys. Drug Discov. Today 8(15), 681-691 (2003).
24. Hu Y, Bajorath J. How promiscuous are pharmaceutically relevant compounds? A data-driven assessment. AAPS J. 15(1), 104-111 (2013).
25. Gaulton A, Bellis LJ, Bento AP et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100-D1107 (2012).
26. Gaulton A, Hersey A, Nowotka M et al. The ChEMBL database in 2017. Nucleic Acids Res. 45(D1), D945-D954 (2017).
27. Wang Y, Xiao J, Suzek TO et al. PubChem's BioAssay database. Nucleic Acids Res. 40, D400-D412 (2012).
28. Law V, Knox C, Djoumbou Y et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 42, D1091-D1097 (2014).
29. OEChem TK. OpenEye Scientific Software, Inc., NM, USA (2012). www.eyesopen.com/
30. Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28(1), 31-36 (1988).
31. UniProt Consortium. The Universal Protein Resource (UniProt) in 2010. Nucleic Acids Res. 38, D142-D148 (2010).
32. Kenny PW, Sadowski J. Structure modification in chemical databases. In: Chemoinformatics in Drug Discovery. Oprea TI (Ed.). Wiley-VCH, Weinheim, Germany, 271-285 (2004).
33. Hussain J, Rea C. Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J. Chem. Inf. Model. 50(3), 339-348 (2010).
34. Hu X, Hu Y, Vogt M, Stumpfe D, Bajorath J. MMP-cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs. J. Chem. Inf. Model. 52(5), 1138-1145 (2012).
35. Sterling T, Irwin JJ. ZINC 15 - ligand discovery for everyone. J. Chem. Inf. Model. 55(11), 2324-2337 (2015).
36. Reddy AS, Zhang S. Polypharmacology: drug discovery for the future. Expert Rev. Clin. Pharmacol. 6(1), 41-47 (2013).
37. Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3(8), 673-683 (2004).
38. Chong CR, Sullivan DJ. New uses for old drugs. Nature 448(7154), 645-646 (2007).
39. Fechner N, Papadatos G, Evans D et al. ChEMBLSpace - a graphical explorer of the chemogenomic space covered by the ChEMBL database. Bioinformatics 29(4), 523-524 (2013).
40. Carrascosa MC, Massaguer OL, Mestres J. PharmaTrek: a semantic web explorer for open innovation in multitarget drug discovery. Mol. Inform. 31(8), 537-541 (2012).
41. Canny SA, Cruz Y, Southern MR, Griffin PR. PubChem promiscuity: a web resource for gathering compound promiscuity data from PubChem. Bioinformatics 28(1), 140-141 (2012).
42. Howe EA, de Souza A, Lahr DL et al. BioAssay Research Database (BARD), chemical biology and probe-development enabled by structured metadata and result types. Nucleic Acids Res. 43, D1163-D1170 (2015).
43. von Eichborn J, Murgueitio MS, Dunkel M, Koerner S, Bourne PE, Preissner R. PROMISCUOUS: a database for network-based drug-repositioning. Nucleic Acids Res. 39, D1060-D1066 (2011).
44. Mestres J, Gregori-Puigjané E, Valverde S, Solé RV. Data completeness - the Achilles heel of drug-target networks. Nat. Biotechnol. 26(9), 983-984 (2008).
45. Mestres J, Gregori-Puigjané E, Valverde S, Solé RV. The topology of drug-target interaction networks: implicit dependence on drug properties and target families. Mol. Biosyst. 5(9), 1051-1057 (2009).
46. Hu Y, Bajorath J. Monitoring drug promiscuity over time. F1000Research 3, 218 (2014).
47. Han L, Wang Y, Bryant SH. A survey of across-target bioactivity results of small molecules in PubChem. Bioinformatics 25(17), 2251-2255 (2009).
48. Hu Y, Bajorath J. High-resolution view of compound promiscuity. F1000Research 2, 144 (2013).
49. Hu Y, Jasial S, Bajorath J. Promiscuity progression of bioactive compounds over time. F1000Research 4, 118 (2015).
50. Bredel M, Jacoby E. Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat. Rev. Genet. 5(4), 262-275 (2004).
51. Jasial S, Hu Y, Bajorath J. Determining the degree of promiscuity of extensively assayed compounds. PLoS ONE 11(4), e0153873 (2016).
52. Knight ZA, Lin H, Shokat KM. Targeting the cancer kinome through polypharmacology. Nat. Rev. Cancer 10(2), 130-137 (2010).
53. Morphy R. Selectively nonselective kinase inhibition: striking the right balance. J. Med. Chem. 53(4), 1413-1437 (2009).
54. Hu Y, Furtmann N, Bajorath J. Current compound coverage of the kinome. J. Med. Chem. 58(1), 30-40 (2015).
55. Duffner JL, Clemons PA, Koehler AN. A pipeline for ligand discovery using small-molecule microarrays. Curr. Opin. Chem. Biol. 11(1), 74-82 (2007).