QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network

摘要

Computer-based quantitative structure-activity relationship (QSAR) model has been becoming a pow- erful tool in understanding the structural requirements for chemicals to bind the estrogen receptor (ER), designing drugs for human estrogen replacement therapy, and identifying potential estrogenic endo- crine disruptors. In this study, a simple yet powerful neural network technique, generalized regression neural network (GRNN) was used to develop a QSAR model based on 131 structurally diverse estro- gens (training set). Only nine descriptors calculated solely from the molecular structures of com- pounds selected by objective and subjective feature selections were used as inputs of the GRNN model. The predictive power of the built model was found to be comparable to that of the more traditional techniques but requiring significantly easy implementation and a shorter computation-time. The ob- tained result indicates that the proposed GRNN model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogenic activity of organic compounds.