An Integrated Soft Computing Approach for Predicting Biological Activity of Potential HIV-1 Protease Inhibitors

Using a neural network-fuzzy logic-genetic algorithm approach we generate an optimal predictor for biological activities of HIV-1 protease potential inhibitory compounds. We use genetic algorithms (GAs) in the two optimization stages. In the first stage, we generate an optimal subset of features. In the second stage, we optimize the architecture of the fuzzy neural network. The optimized network is trained and used for the prediction of biological activities of newly designed chemical compounds. Finally, we extract fuzzy IF/THEN rules. These rules map physico-chemical structure descriptors to predicted inhibitory values. The optimal subset of features, combined with the generated rules, can be used to analyze the influence of descriptors.