Title:ANN and Bayesian Classification Models for Virtual Screening of Endocrine-Disrupting Chemicals
VOLUME: 17 ISSUE: 5
Author(s):Piotr Nowicki, Jolanta Klos and Zenon Kokot
Affiliation:Department of Inorganic and Analytical Chemistry, Poznan University of Medical Sciences, Faculty of Pharmacy, Grunwaldzka 6, 60-780 Poznan, Poland.
Keywords:ANN, bayesian categorization model, endocrine disruptor, endocrine disruptors knowledge base, molecular
fingerprints, QSAR, virtual screening, molecular descriptors.
Abstract:The identification of endocrine-disrupting chemicals (EDCs) is one of the important goals of environmental
chemical hazard screening. The adverse health effects of EDCs in humans have been demonstrated to involve the
developmental, reproductive, neurological, cardiovascular, metabolic, and immune systems.
The present study reports QSAR classification studies on a large database comprising 8,212 compounds collected from
the Estrogenic Activity Database and National Center for Biotechnology Information Database. In this study, four
classification models (Bayesian Categorization Model with molecular fingerprints or molecular descriptors as an input
and Neural Classification Models with and without Bayesian regularization) were used. Evaluation of these binomial
classification methods indicated that the Bayesian method (Bayesian QSAR) works as an excellent method for prediction
with fingerprints used as input. In the case of the multilayer perceptron with molecular descriptors as inputs, changing the
training mode by introducing a Bayesian regularization algorithm significantly improved ANNs’ predictive power. Our
goal was to test two popular classification methods suitable for processing large data sets. Such datasets were required to
ensure the prediction performances and applicability of the models as a virtual screening tool for an extensive database.
