Prediction of anticancer activity of aliphatic nitrosoureas using quantum chemical quantitative structure activity relation (QSAR) methods

Abstract

Design and development of new anticancer drugs with low toxicity is a very challenging task and computer aided methods are being increasingly used to solve this problem. In this study, we investigated the anticancer activity of aliphatic nitrosoureas using quantum chemical quantitative structure activity relation (QSAR) approach. In this method, the physic-chemical properties, known as descriptors, necessary for predicting quantitative structure activity relations was obtained from semi empirical quantum chemical methods. We used Recife Model 1 to optimize the structure of the molecules and to calculate the quantum chemical descriptors, while heuristic and best multilinear regression methods were applied to obtain the best correlation. Two data sets containing aliphatic nitrosoureas and chloroethyl substituted nitrosoureas were used in the present calculations. The QSAR equations obtained here can be used to design new anticancer drugs prior to resorting to experimental activity studies.

Key words: Quantitative structure activity relationship (QSAR), best multi linear regression (BMLR), quantum chemical method, Recife Model 1 (RM1).