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The Fuzzy ARTMAP with Relevance factor (FAMR) is a Fuzzy ARTMAP (FAM) neural architecture with the following property: Each training pair has a relevance factor assigned to it, proportional to the importance of that pair during the learning phase. Using a relevance factor adds more flexibility to the training phase, allowing ranking of sample pairs according to the confidence we have in the information source. We focus on the prediction of biological activities of HIV- 1 protease inhibitory compounds, both known and novel, using a FAMR model. Our new approach consists of two stages: i) During the first stage, we use a genetic algorithm (GA) to optimize the relevances assigned to the training data. This improves the generalization capability of the FAMR. ii) In the second stage we use the optimized relevances to train the FAMR. Finally, the trained FAMR is used to predict the biological activities of newly designed potential HIV-1 protease inhibitors.