The Fuzzy ARTMAP with Relevance factor (FAMR) is a Fuzzy ARTMAP (FAM) neural architecture with the fol- lowing 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 in- formation 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 algo- rithm (GA) to optimize the relevances assigned to the train- ing data. This improves the generalization capability of the FAMR. ii) In the second stage we use the optimized rele- vances to train the FAMR. Finally, the trained FAMR is used to predict the biological activities of newly designed poten- tial HIV-1 protease inhibitors.