Consensus kNN QSAR: A Versatile Method for Predicting the Estrogenic Activity of Organic Compounds In Silico. A Comparative Study with Five Estrogen Receptors and a Large, Diverse Set of Ligands

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Abstract

Quantitative structure-activity relationships (QSARs) have proved increasingly useful for predicting the biological activities of molecules (e.g., their binding affinities to different receptors) and can be used in environmental chemistry as a preliminary tool for screening the activities of untested molecules, producing valuable information on which compounds should be tested more thoroughly with experimental affinity assays or in animals. The predictive ability of the consensus kNN QSAR method is corroborated here using a diverse set of 245 compounds, which have been assayed for their relative binding affinities to the estrogen receptor of four species: human (ER alpha and ER beta), calf, mouse, and rat. Leave-one-out cross-validation (LOO-CV) and gamma-randomization tests were applied to the QSAR models for internal validation, and separate training and test sets were used for external validation. The internal predictive abilities of the consensus models for all five data sets were convincing, with cross-validated correlation coefficients (LOO-CV q2 values) varying from 0.69 (human ER beta data) to 0.79 (human ER alpha data). The external predictive abilities were also encouraging, as the predictive r2 scores (pr-r2 values) varied from 0.62 (human ER beta data) to 0.77 (calf and mouse data). The results indicate that consensus kNN QSAR is a feasible method for rapid screening of the estrogenic activity of organic compounds.

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