title: Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities.
abstract: Structure-property relationships and structure-activity relationships play an important role in many research areas, such as medicinal chemistry and drug discovery. Such methods, however, have focused on providing post-hoc descriptions of such relationships based on known data. The ability for these descriptions to remain relevant when considering compounds of unknown activity, and thus the prediction of activity and property landscapes using existing data, remains little explored. In this study, we present a novel method of evaluating the ability of a compound comparison methodology to provide accurate information about a set of unknown compounds and also explore the ability of these predicted activity landscapes to prioritize active compounds over inactive. These methods are applied to three distinct and diverse sets of compounds, each with activity data for multiple targets, for a total of eight target-compound set pairs. Six methodologically distinct compound comparison methods were evaluated. We show that overall, all compound comparison methods provided an improvement in structure-activity relationship prediction over random and were able to prioritize compounds in a superior manner to random sampling, but the degree of success and therefore applicability varied markedly.
authors: Torrey Pines Institute for Molecular Studies , 11350 SW Village Parkway, Port St. Lucie, Florida 34987, United States.
authors: Radleigh G
authors: RG
authors: Santos
authors: Marc A
authors: MA
authors: Giulianotti
authors: Richard A
authors: RA
authors: Houghten
authors: José L
authors: JL
authors: Medina-Franco
mesh_terms: D000465:Algorithms; D000981:Antiprotozoal Agents / Q000737:chemistry*; D003198:Computer Simulation; D062126:Databases, Chemical; D055808:Drug Discovery; D006801:Humans; D008024:Ligands; D008956:Models, Chemical; D015233:Models, Statistical; D015394:Molecular Structure; D009292:Narcotic Antagonists; D011957:Receptors, Opioid / Q000819:agonists / Q000737:chemistry*; D054852:Small Molecule Libraries / Q000737:chemistry*; D013329:Structure-Activity Relationship; D000094942:Nociceptin Receptor
chemical_list: D000981:Antiprotozoal Agents; D008024:Ligands; D009292:Narcotic Antagonists; D011957:Receptors, Opioid; D054852:Small Molecule Libraries; D000094942:Nociceptin Receptor; C000722283:OPRL1 protein, human
references: J Chem Inf Model. 2011 Feb 28;51(2):258-66
references: 21275393
references: J Comb Chem. 2008 Jan-Feb;10(1):3-19
references: 18067268
references: J Chem Inf Model. 2012 Mar 26;52(3):792-803
references: 22360769
references: Drug Discov Today. 2010 Aug;15(15-16):630-9
references: 20547243
references: Expert Opin Drug Discov. 2012 Jun;7(6):463-73
references: 22475223
references: J Chem Inf Model. 2008 Mar;48(3):646-58
references: 18303878
references: Drug Discov Today. 2009 Jul;14(13-14):698-705
references: 19410012
references: J Chem Inf Model. 2012 Jun 25;52(6):1607-20
references: 22646988
references: J Chem Inf Model. 2012 Aug 27;52(8):2181-91
references: 22873578
references: Curr Med Chem. 2000 Feb;7(2):141-58
references: 10637360
references: Methods Mol Biol. 2011;672:39-100
references: 20838964
references: Curr Med Chem. 2009;16(32):4297-313
references: 19754417
references: J Chem Inf Model. 2011 Sep 26;51(9):2427-39
references: 21842860
references: Chem Biol Drug Des. 2007 Nov;70(5):393-412
references: 17927720
references: Bioorg Med Chem. 2012 Sep 15;20(18):5366-71
references: 22484008
references: J Chem Inf Model. 2011 Jun 27;51(6):1259-70
references: 21609014
references: J Chem Inf Model. 2012 Sep 24;52(9):2454-61
references: 22856455
references: Mol Inform. 2010 Jul 12;29(6-7):476-88
references: 27463326
references: J Chem Inf Model. 2012 Oct 22;52(10):2485-93
references: 22989212
references: Bioorg Med Chem. 2010 Nov 1;18(21):7380-91
references: 20888242
references: J Chem Inf Model. 2012 Sep 24;52(9):2354-65
references: 22894655
references: J Med Chem. 2010 Dec 9;53(23):8209-23
references: 20845971