Comparative QSAR based on neural networks for the anti-HIV activity of HEPT derivatives.

Among the non-nucleoside reverse transcriptase inhibitors, 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives have proved to be potent and selective inhibitors of human immunodeficiency virus (HIV-1). They are able to completely suppress virus replication in cell cultures. The quantitative structure-activity relationships (QSAR) try to describe the association between biological activities of a group of congeners and their molecular descriptors. In this paper, recent works on the application of neural networks (NN) and multiple regression analyses to quantitative structure-anti-HIV activity of HEPT derivatives are reviewed. NN have their origins in efforts to reproduce computer models of the information processing that takes place in the brain. They have found application in a wide variety of fields, such as image analysis of facial features, stock market predictions, etc. Application of the NN methods to problems in chemistry and biochemistry has rapidly gained popularity in recent years. We briefly describe a methodology for designing NN for QSAR and estimating their performances, and apply this approach to the prediction of anti-HIV activity of HEPT. The predictive power of the NN used is compared with that of other statistical methods.