Improving the Feature Selection for the Development of Linear Model for Discovery of HIV-1 Integrase Inhibitors

摘要

Dimeric aryl β- diketo acids have proven to be effective inhibitors to the HIV strand transfer mechanism of HIV-integrase. In order to create the best drug to fight HIV-integrase, it is important to know which features of the diketo acids have the biggest impact on reducing HIV enzyme activity. In this research, the development of the Differential Evolutionary Binary Particle Swarm Optimization (DE-BPSO) algorithm with a Multiple Linear Regression (DE-BPSO/MLR) model is discussed and compared with the results against linear models tested in previous research. The use of both of evolutionary algorithms and predictive models, such as the differential evolutionary - binary particle swarm optimization (DE-BPSO) algorithm can help find a subset of the diketo acid's chemical descriptors that are best able to predict the reduction in HIV-integrase enzyme activity by more than 50%.