Design of drugs with potential antitumoral activity

Abstract

Cancer is a health problem, resulting in the first cause of death worldwide. In the present paper, a Quantitative Structure-Activity Relationship (QSAR) study was developed as a tool for the design of new drugs with potential antitumor activity. Discriminant Linear Analysis and Neural Network were the mathematics methods used to estimate the activity of in a data set consisting in 300 compounds. The biological activity, extracted from the US National Cancer Institute was divided by cluster analysis in a training and prediction series. A model with 10 variables and 84,33 % of correct classification was obtained by a discriminant function meanwhile, the neural network tested with the same number of variables resulted in a 89,67 % of accuracy. Also was calculated the contribution of different structural fragments on the cytostatic activity, and quantified their contribution. Six new compounds were designed predicting a good antitumor activity. In general, the predictive quality of the neural network model was higher than the linear discriminant.

Keywords: cancer, QSAR study, antitumor activity.