QSAR modeling of aromatase inhibition by flavonoids using machine learning approaches

Abstract

AbstractAromatase is a member of the cytochrome P450 family responsible for catalyzing the rate-limiting conversion of androgens to estrogens. In the pursuit of robust aromatase inhibitors, quantitative structure-activity relationship (QSAR) and classification structure-activity relationship (CSAR) studies were performed on a non-redundant set of 63 flavonoids using multiple linear regression, artificial neural network, support vector machine and decision tree approaches. Easy-to-interpret descriptors providing comprehensive coverage on general characteristics of molecules (i.e., molecular size, flexibility, polarity, solubility, charge and electronic properties) were employed to describe the unique physicochemical properties of the investigated flavonoids. QSAR models provided good predictive performance as observed from their statistical parameters with Q values in the range of 0.8014 and 0.9870 for the cross-validation set and Q values in the range of 0.8966 and 0.9943 for the external test set. Furthermore, CSAR models developed with the J48 algorithm are able to accurately classify flavonoids as active and inactive as observed from the percentage of correctly classified instances in the range of 84.6 % and 100 %. The study presented herein represents the first large-scale QSAR study of aromatase inhibition on a large set of flavonoids. Such investigations provide an important insight on the origins of aromatase inhibitory properties of flavonoids as breast cancer therapeutics.