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.
Disclaimer
The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the US Environmental Protection Agency.
Financial & competing interests disclosure
O Nicolotti and GF Mangiatordi wish to acknowledge FIRB (Futuro in Ricerca 2012, RBFR12SJA8_003) and IDEA 2011 (GRBA11EB3G). E Benfenati would like to thank LIFE+ project EDESIA for funding. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.
No writing assistance was utilized in the production of this manuscript.