A new approach is presented for the development of quantitative structure-property relations (QSPR) based on the extraction of relevant molecular features with self-organizing maps and the use of a modified fuzzy-ARTMAP classifier for variable prediction. The present methodology is demonstrated for the development of a QSPR for the aqueous-phase infinite dilution activity coefficient gamma(infinity), based on a data set of 325 diverse organic compounds. The 4 QSPR was developed using a set of 11 molecular descriptors (four connectivities X-v(1-4), Coulomb self-similarity measure, electron-nuclear attraction, dipole moment, sum of atomic numbers, number of filled levels, average polarizability, and nuclear-nuclear repulsion). The final set of molecular descriptors was selected from an initial pool of 23 topological and quantum chemical descriptors, including six molecular quantum similarity measures, by means of a topological analysis of self-organization of the data set. Additional interpolated information to enhance the training of the neural system was obtained from the self-organization analysis. The resulting fuzzy-ARTMAP-based QSPRs performed with errors that were on the average seven times smaller compared to previous published models. The use of only four molecular quantum similarity measures proved to be sufficient for building a lngamma(infinity) fuzzy-ARTMAP-based QSPR with reasonable accuracy. (C) 2004 American Institute of Chemical Engineers.