QSAR prediction of HIV inhibition activity of styrylquinoline derivatives by genetic algorithm coupled with multiple linear regressions

Abstract

In spite of significant progress in anti-HIV-1 therapy, current antiviral chemotherapy still suffers from deleterious side effects and emerging drug resistance. Styrylquinoline derivative compounds have been shown to inhibit IN integration activity in vitro and to block viral replication at non-toxic concentrations. To understand the pharmacophore properties of styrylquinoline derivatives and to design inhibitors of HIV-1 integrase quantitative structure–activity relationships (QSAR) were developed using a descriptor selection approach that is based on the genetic algorithm (GA). The biological activity of styrylquinoline derivative molecules was efficiently estimated and predicted with the QSAR model. The most important descriptors were selected from a set of Dragon descriptors after pre-selection to build the QSAR model, using the multiple linear regressions (MLRs). The predictive quality of the QSAR models was tested for an external set of compounds, which were not used in the model development stage. The results demonstrated that GA–MLR is a simple and fast methodology for styrylquinoline derivatives modeling.