McQSAR: A Multiconformational Quantitative Structure−Activity Relationship Engine Driven by Genetic Algorithms

Abstract

The generation of quantitative structure-activity relationships (QSARs) under the supervision of a genetic algorithm (GA) is a QSAR modeling approach used for more than a decade. In this paper we present McQSAR, an extension to the traditional GA approach to derive QSARs. McQSAR is able to use descriptors for multiple representations per compound, such as different conformers, tautomers, or protonation forms. Test runs show that the algorithm converges to a set of representations that describe the binding mode of the set of input molecules to a reasonable resolution provided that suitable descriptors-based on the three-dimensional structure-are used. Furthermore, the frequency of chance correlation was measured during multiple runs on a real-life data set using simulated linear relationship functions. The observed frequency of chance correlation, on average 0.3 +/- 0.5%, was found independent of the size of the calibration set and the number of terms in the underlying relationship function.