Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data. (September 2015)

• Record Type: Journal Article
• Authors: Trisciuzzi, Daniela; Alberga, Domenico; Mansouri, Kamel; Judson, Richard; Cellamare, Saverio; Catto, Marco; Carotti, Angelo; Benfenati, Emilio; Novellino, Ettore; Mangiatordi, Giuseppe Felice; Nicolotti, Orazio
• Is Part Of: Future medicinal chemistry. Volume 7:Number 14(2015)
• Page Start: 1921
• Page End: 1936
• Publication Date: 2015-09
• Subjects: Pharmaceutical chemistry -- Periodicals; 615.19005
• DOI: 10.4155/fmc.15.103
• Languages: English
• ISSNs: 1756-8919

Abstract

Background: The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals.Results: The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Moreover, external predictions were made successfully on ten representative known estrogenic chemicals and on a set consisting of >32, 000 chemicals.Conclusion: The authors demonstrate that structure-based methods, widely applied to drug discovery programs, can be fairly adapted to exploratory toxicology studies.