ChemInform Abstract: Fuzzy ARTMAP and Back-Propagation Neural Networks Based Quantitative Structure-Property Relationships (QSPRs) for Octanol-Water Partition Coefficient of Organic Compounds

Quantitative structure−property relationships (QSPRs) for estimating the logarithm octanol/water partition coefficients, logKow, at 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 MO-theory. 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 logKow for the test and validation data sets, respectively, over the same range of logKow values....