QSAR Modeling of Anti-HIV Activities of Alkenyldiarylmethanes Using Topological and Physicochemical Descriptors

Abstract

Three series of anti-HIV data (reverse transcriptase inhibitory activity, cytopathicity data, and cytotoxicity data) of alkenyldiarylmethanes were modeled with physicochemical, topological and structural descriptors by multiple regression analysis using principal component factor analysis as the data pre-processing step. Molar refractivity was found to be a significant contributor in modeling all three data sets. Apart from this, partition coefficient, E-state index, valence connectivity and indicator parameters were important in modeling different activity series. The final relations were of moderate to good quality as evidenced from regression statistics (R2 values ranging 66-75%) and leave-one-out cross validation data (Q2 values ranging 54-70%).