Deriving Quantitative Structure-Activity Relationship Models Using Genetic Programming for Drug Discovery

Abstract

Genetic Programming is a heuristic search algorithm inspired by evolutionary techniques that has been shown to produce satisfactory solutions to problems related to several scientific domains [1]. Presented here is a methodology for the creation of Quantitative Structure-Activity Relationship (QSAR) models for the prediction of chemical activity, using Genetic Programming. QSAR analysis is crucial for drug discovery since good QSAR models enable human experts to select compounds with increased chances of being active for further investigations. Our technique has been tested using the Selwood dataset, a benchmark dataset for the QSAR field [2]. The results indicate that the QSAR models created are accurate, reliable and simple and can thus be used to identify molecular descriptors correlated with measured activity and for the prediction of the activity of untested molecules. The QSAR models we generated predict the activity of untested molecules with an error ranging between 0.46 -0.8 on the scale [-1,1]. These results compare favourably with results sited in the literature for the same dataset [3], [4], Our models are constructed using any combination of the arithmetic operators {+, -, /, *}, the descriptors available and constant values.