A Fuzzy ARTMAP-Based Quantitative Structure−Property Relationship (QSPR) for the Henry's Law Constant of Organic Compounds

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Abstract
Quantitative structure-property relationships (QSPRs) for estimating a dimensionless Henry's Law constant of organic compounds at 25 degrees C were developed based on a fuzzy ARTMAP and back-propagation neural networks using a heterogeneous set of 495 organic compounds. A set of molecular descriptors developed from PM3 semiempirical MO-theory and topological descriptors (second-order molecular connectivity index) were used as input parameters to the neural networks. Quantum chemical input descriptors included average molecular polarizability, dipole moments (total point charge, total hybridization, and total sum), ionization potential, and heat of formation. The fuzzy ARTMAP/QSPR correlated Henry's Law constant for -6.72 </= logH </= 2.87 with average absolute errors of 0.03 and 0.13 logH units for the overall data and the test set, respectively. The optimal 7-17-1 back-propagation/QSPR model was less accurate and exhibited larger average absolute errors of 0.28 and 0.27 logH units for the validation (recall) and test sets, respectively. The fuzzy ARTMAP-based QSPR was superior to the back-propagation and multiple linear regression/QSPR models for Henry's Law constant of organic compounds.