Quantitative structure-property relationships (QSPRs) for estimating the logarithm octanol/water partition coefficients, logKow ,a t 25°C were developed based on fuzzy ARTMAP and back-propagation neural networks using a heterogeneous set of 442 organic compounds. The set of molecular descriptors were derived from molecular connectivity indices and quantum chemical descriptors calculated from PM3 semiempirical MOtheory. Quantum chemical input descriptors include average polarizability, dipole moments, exchange energy, total electrostatic interaction energy, total two-center energy, and ionization potential. The fuzzy ARTMAP/ QSPR performed, for a logKow range of -1.6 to 7.9, with average absolute errors of 0.03 and 0.14 logKow for the overall data and test sets, respectively. The optimal 12 -11-1 back-propagation/QSPR model, for the same range of logKow, exhibited larger average absolute errors of 0.23 and 0.27 log Kow for the test and validation data sets, respectively, over the same range of log Kow values. The present results with the fuzzy ARTMAP-based QSPR are encouraging and suggest that high performance logKow QSPR that encompasses a wider range of chemical groups could be developed, following the present approach, by training with a larger heterogeneous data set.
['Denise Yaffe', 'Yoram Cohen', 'Gabriela Espinosa', 'Alex Arenas', 'Francesc Giralt']
Fuzzy ARTMAP and Back-Propagation Neural Networks Based Quantitative Structure-Property Relationships (QSPRs) for Octanol-Water Partition Coefficient of Organic Compounds