Application of GFA-MLR and G/PLS techniques in QSAR/QSPR studies with application in medicinal chemistry and predictive toxicology

Abstract

Quantitative structure-activity/property/toxicity relationship (QSAR/QSPR/QSTR) models enable predictions of activity/property/toxicities to be made directly from the chemical structure. Feature selection is one of the integral parts in the development of QSAR/QSPR models which is also included in the Organization of Economic Co-operation and Development (OECD) principle of "an unambiguous algorithm" for QSAR model development and validation. Genetic algorithm (GA) based on the principle of Darwin’s theory of natural selection and evolutions are being widely used in recent times for the selection of descriptors in the development of predictive models for toxicity assessment and virtual screening of hazardous chemicals and design of drug compounds with therapeutic activity. The GA algorithm can handle a huge number of descriptors and generate a population of models competitive with or superior to the results of standard regression analysis. Genetic function approximation (GFA) involves the combination of multivariate adaptive regression splines (MARS) algorithm of Friedman with genetic algorithm of Holland to evolve population of equations. GFA calculations are based on three operators: selection, crossover and mutation. Using spline based terms in the model construction, GFA can either remove the outlier compounds or identify a range of effect. GFA followed by multiple linear regression (GFA-MLR) or partial least squares (G/PLS) regression is frequently used by different research groups for the development of predictive QSAR/QSPR models. This chapter presents examples of some case studies of the use of GFA-MLR and G/PLS techniques in developing predictive models in medicinal chemistry and predictive toxicology applications.