A fuzzy neural network (FNN) was trained on a small dataset of HIV-1 protease ligands with experimentally measured IC50 values.  A set of descriptors was selected to build nonlinear quantitative structure-activity relationships.  A genetic algorithm (GA) was implemented to optimize the architecture of the fuzzy neural network used to predict biological activity of HIV-1 protease inhibitors.  Evolutionary methods were used to apply feature selection (FS) to this model.  Results obtained on an external test set, with and without feature selection, were compared.  Fuzzy IF/THEN rules, which display quantitative structure-activity relationships, were extracted from the optimized FNN.

Fuzzy neural network analysis of the affinity of inhibitors for HIV-1 protease

243rd National Meeting (2012)

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