A nonlinear quantitative structure-anti-HIV-1-activity relationship (QSAR) study was investigated in a series of 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine] (HEPT) derivatives acting as nonnucleoside reverse transcriptase inhibitors (NNRTIs). This QSAR study has been undertaken by a three-layered neural network (NN) using molecular descriptors known to be responsible for the anti-HIV-1 activity. The usefulness of the model and the nonlinearity of the relationship between molecular descriptors and anti-HIV-1 activity have been clearly demonstrated. The obtained model outperforms those given in the literature in both the fitting and predictive stages. NN analysis yielded predicted activities in excellent agreement with the experimentally obtained values (R(2) = 0.977, predictive r(2) = 0.862). The effect of each molecular feature on the anti-HIV-1 activity variation has been clearly elucidated.

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.

Structure-anti HIV activity relationships were established for a sample of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio)thymine (HEPT) using a three-layer neural network (NN). Eight structural descriptors and physicochemical variables were used to characterize the HEPT derivatives under study. The network's architecture and parameters were optimized in order to obtain good results. All the NN architectures were able to establish a satisfactory relationship between the molecular descriptors and the anti-HIV activity. NN proved to give better results than other models in the literature. NN have been shown to be particularly successful in their ability to identify non-linear relationships.

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