Automated design of ligands to polypharmacological profiles

Nature

Volumn 492, Issue 7428, 2012, Pages 215-220

  Besnard, Jérémy ^a   Ruda, Gian Filippo ^a   Setola, Vincent ^b   Abecassis, Keren ^a   Rodriguiz, Ramona M ^c   Huang, Xi Ping ^b   Norval, Suzanne ^a   Sassano, Maria F ^b   Shin, Antony I ^c   Webster, Lauren A ^a   Simeons, Frederick R C ^a   Stojanovski, Laste ^a   Prat, Annik ^d   Seidah, Nabil G ^d   Constam, Daniel B ^e   Bickerton, G Richard ^a   Read, Kevin D ^a   Wetsel, William C ^c   Gilbert, Ian H ^a   Roth, Bryan L ^b   Hopkins, Andrew L ^a   more..

^a UNIVERSITY OF DUNDEE   (United Kingdom)

^b UNIVERSITY OF NORTH CAROLINA   (United States)

^c DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d CLIN RESEARCH INSTITUTE OF MONTREAL   (Canada)

^e EPFL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

CHOLINESTERASE INHIBITOR; DONEPEZIL; G PROTEIN COUPLED RECEPTOR; LIGAND; PROTEOME;

AUTOMATION; BRAIN; DRUG; ENZYME ACTIVITY; INHIBITOR; LIGAND; POLYMER; PROTEIN; PROTEOMICS;

ARTICLE; AUTOMATION; BLOOD BRAIN BARRIER; CENTRAL NERVOUS SYSTEM; COMPLEX FORMATION; DRUG DESIGN; DRUG EFFICACY; DRUG RECEPTOR BINDING; DRUG SAFETY; DRUG SELECTIVITY; DRUG STRUCTURE; EVOLUTION; HUMAN; LIGAND BINDING; NONHUMAN; PRIORITY JOURNAL; PROTEIN TARGETING;

ANIMALS; AUTOMATION; DRUG DELIVERY SYSTEMS; DRUG DESIGN; FEMALE; LIGANDS; MALE; MICE; MICE, INBRED C57BL; MODELS, THEORETICAL; PHARMACOLOGICAL PHENOMENA; REPRODUCIBILITY OF RESULTS;

EID: 84870987376     PISSN: 00280836     EISSN: 14764687     Source Type: Journal    
DOI: 10.1038/nature11691     Document Type: Article

References (58)

1. Hughes, J. D. et al. Physicochemical drug properties associated with in vivo toxicological outcomes. Bioorg. Med. Chem. Lett. 18, 4872-4875 (2008).
2. Roth, B. L. Drugs and valvular heart disease. N. Engl. J. Med. 356, 6-9 (2007).
3. Campillos, M., Kuhn, M., Gavin, A. C., Jensen, L. J. & Bork, P. Drug target identification using side-effect similarity. Science 321, 263-266 (2008).
4. Roth, B. L., Sheffler, D. J. & Kroeze, W. K. Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nature Rev. Drug Discov. 3, 353-359 (2004).
5. Knight, Z. A., Lin, H. & Shokat, K. M. Targeting the cancer kinome through polypharmacology. Nature Rev. Cancer 10, 130-137 (2010).
6. Brö tz-Oesterhelt, H. & Brunner, N. A. How many modes of action should an antibiotic have? Curr. Opin. Pharmacol. 8, 564-573 (2008).
7. Hopkins,A. L. Network pharmacology: the next paradigmin drug discovery. Nature Chem. Biol. 4, 682-690 (2008).
8. Morphy, R. & Rankovic, Z. Designed multiple ligands. An emerging drug discovery paradigm. J. Med. Chem. 48, 6523-6543 (2005).
9. Paolini, G. V., Shapland, R. H. B., vanHoorn,W. P.,Mason, J. S.&Hopkins, A. L. Global mapping of pharmacological space. Nature Biotechnol. 24, 805-815 (2006).
10. Keiser, M. J. et al. Relating protein pharmacology by ligand chemistry. Nature Biotechnol. 25, 197-206 (2007).
11. Keiser, M. J. et al. Predicting new molecular targets for known drugs. Nature 462, 175-181 (2009).
12. Vidal, D. & Mestres, J. In silico receptorome screening of antipsychotic drugs. Mol. Inf. 29, 543-551 (2010).
13. Lounkine, E. et al. Large-scale prediction and testing of drug activity on side-effect targets. Nature 486, 361-367 (2012).
14. Schneider,G.&So, S.-S. Adaptive Systems inDrugDesign (LandesBiosciences, 2002).
15. Schneider, G. et al. Voyages to the (un)known: adaptive design of bioactive compounds. Trends Biotechnol. 27, 18-26 (2009).
16. Schneider, G., Lee, M. L., Stahl, M. & Schneider, P. De novo design of molecular architectures by evolutionary assembly of drug-derivedbuilding blocks. J.Comput. Aided Mol. Des. 14, 487-494 (2000).
17. Gillet, V. J., Willett, P., Fleming, P. J. & Green, D. V. Designing focused libraries using MoSELECT. J. Mol. Graph. Model. 20, 491-498 (2002).
18. Brown, N.,McKay, B.&Gasteiger, J.The de novo design ofmedianmolecules within a property range of interest. J. Comput. Aided Mol. Des. 18, 761-771 (2004).
19. Nicolaou, C. A., Brown, N. & Pattichis, C. S. Molecular optimization using computational multi-objective methods. Curr. Opin. Drug Discov. Devel. 10, 316-324 (2007).
20. Liu, Q., Masek, B., Smith, K. & Smith, J. Tagged fragment method for evolutionary structure-based de novo lead generation and optimization. J. Med. Chem. 50, 5392-5402 (2007).
21. Dey, F. & Caflisch, A. Fragment-based de novo ligand design by multiobjective evolutionary optimization. J. Chem. Inf. Model. 48, 679-690 (2008).
22. Vinkers, H. M. et al. SYNOPSIS: SYNthesize and OPtimize system in silico. J. Med. Chem. 46, 2765-2773 (2003).
23. Heikkilä, T. et al. The first de novo designed inhibitors of Plasmodium falciparum dihydroorotate dehydrogenase. Bioorg. Med. Chem. Lett. 16, 88-92 (2006).
24. Roche, O. & Rodríguez Sarmiento, R. M. A new class of histamine H3 receptor antagonists derived from ligand based design. Bioorg. Med. Chem. Lett. 17, 3670-3675 (2007).
25. Alig, L. et al. Benzodioxoles: novel cannabinoid-1 receptor inverse agonists for the treatment of obesity. J. Med. Chem. 51, 2115-2127 (2008).
26. Schneider, G. et al. Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors. Future Med. Chem. 3, 415-424 (2011).
27. Wermuth, C. G. Selective optimization of side activities: the SOSA approach. Drug Discov. Today 11, 160-164 (2006).
28. Gaulton, A. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100-D1107 (2012).
29. Ribeiz, S. R. et al. Cholinesterase inhibitors as adjunctive therapy in patients with schizophrenia and schizoaffective disorder: a review and meta-analysis of the literature. CNS Drugs 24, 303-317 (2010).
30. Arnsten, A. F., Murphy, B. & Merchant, K. The selective dopamine D4 receptor antagonist,PNU-101387G, prevents stress-induced cognitive deficits inmonkeys. Neuropsychopharmacology 23, 405-410 (2000).
31. Gillet, V., Johnson, A. P., Mata, P., Sike, S. & Williams, P. SPROUT: a program for structure generation. J. Comput. Aided Mol. Des. 7, 127-153 (1993).
32. Stahl, M. et al. A validation study on the practical use of automated de novo design. J. Comput. Aided Mol. Des. 16, 459-478 (2002).
33. Brown, N., McKay, B., Gilardoni, F. & Gasteiger, J. A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules. J. Chem. Inf. Comput. Sci. 44, 1079-1087 (2004).
34. Nicolaou, C. A., Apostolakis, J. & Pattichis, C. S. De novo drug design using multiobjective evolutionary graphs. J. Chem. Inf. Model. 49, 295-307 (2009).
35. Stewart, K. D., Shiroda, M.& James, C. A. Drug Guru: a computer software program for drug design using medicinal chemistry rules. Bioorg. Med. Chem. 14, 7011-7022 (2006).
36. Kryssanov, V. V., Tamaki, H. & Kitamura, S. Understanding design fundamentals: howsynthesis and analysis drive creativity, resulting inemergence. Artif. Intell. Eng. 15, 329-342 (2001).
37. Deb, K., Sundar, J., Udaya Bhaskara Rao, N.& Chaudhuri, S. Reference point based multi-objective optimization using evolutionary algorithms. Int. J. Comp. Intell. Res. 2, 273-286 (2006).
38. 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 Deliv. Rev. 23, 3-25 (1997).
39. Ertl, P. & Schuffenhauer, A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminform. 10, 8 (2009).
40. Fanelli, F.& De Benedetti, P. G. in Antitargets: Prediction and Prevention of Drug Side Effects (eds Vaz, R. J. & Klabunde, T.) Ch. 8, 155-193 (Wiley-VCH, 2008).
41. Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. molecular frameworks. J. Med. Chem. 39, 2887-2893 (1996).
42. Ortega, R. et al. Synthesis, binding affinity and SAR of new benzolactam derivatives as dopamine D3 receptor ligands. Bioorg. Med. Chem. Lett. 19, 1773-1778 (2009).
43. Löber, S., Hübner, H., Tschammer, N. &Gmeiner, P. Recent advances in the search for D3 and D4 selective drugs: probes, models and candidates. Trends Pharmacol. Sci. 32, 148-157 (2011).
44. Martin, R. E., Green, L. G., Guba, W., Kratochwil, N. & Christ, A. Discovery of the first nonpeptidic, small-molecule, highly selective somatostatin receptor subtype 5 antagonists: a chemogenomics approach. J. Med. Chem. 50, 6291-6294 (2007).
45. Bender, A. et al. Chemogenomic data analysis: prediction of small-molecule targets and the advent of biological fingerprint. Comb. Chem. High Throughput Screen. 10, 719-731 (2007).
46. Rogers, D., Brown, R. D. & Hahn, M. Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up. J. Biomol. Screen. 10, 682-686 (2005).
47. Huang, X. P., Mangano, T., Hufeisen, S., Setola, V. & Roth, B. L. Identification of human Ether-à-go-go related genemodulators by three screening platforms in an academic drug-discovery setting. Assay Drug Dev. Technol. 8, 727-742 (2010).
48. Ruda, G. F. et al. Aryl phosphoramidates of 5-phosho erythronohydroxamic acid,anewclass of potent trypanocidal agents. J.Med.Chem.53,6071-6078(2010).
49. Pogorelov, V. M., Rodriguiz, R. M., Insco, M. L., Caron, M. G. & Wetsel, W. C. Novelty seeking and stereotypic activation of behavior in mice with disruption of the Dat1 gene. Neuropsychopharmacology. 30, 1818-1831 (2005).
50. Porton, B. et al. Mice lacking synapsin III show abnormalities in explicit memory and conditioned fear. Genes Brain Behav. 9, 257-268 (2010).
51. Glick, M., Jenkins, J. L., Nettles, J. H., Hitchings, H. & Davies, J. W. Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and laplacian-modified naive bayesian classifiers. J. Chem. Inf. Model. 46, 193-200 (2006).
52. Rogers, D.& Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 50, 742-754 (2010).
53. Truchon,J.F.&Bayly, C. I.Evaluatingvirtualscreeningmethods: goodandbadmetrics for the "early recognition" problem. J. Chem. Inf. Model. 47, 488-508 (2007).
54. Zhao, W., Hevener, K. E., White, S. W., Lee, R. E. & Boyett, J. M. A statistical framework to evaluate virtual screening. BMC Bioinformatics 10, 225 (2009).
55. Cannon, E. O., Nigsch, F.& Mitchell, J. B. A novel hybrid ultrafast shape descriptor method for use in virtual screening. Chem. Cent. J. 2, 3 (2008).
56. Corne, D. W. & Knowles, J. D. in Proc. 9th Annual Conf. Genetic Evolutionary Computation 773-780 (ACM, 2007).
57. Obrezanova, O., Csanyi, G., Gola, J. M. & Segall, M. D. Gaussian processes: a method for automatic QSAR modelling of ADME properties. J. Chem. Inf. Model. 47, 1847-1857 (2007).
58. Obrezanova, O., Gola, J. M. R., Champness, E. J. & Segall, M. D. Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility. J. Comput. Aided Mol. Des. 22, 431-440 (2008).