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

Abstract

Quantitative structure-property relationships (QSPRs) for estimating a dimensionless Henry's Law constant of organic compounds at 25°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 e logH e 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.

Single-Ion Solvation Free Energies and the Normal Hydrogen Electrode Potential in Methanol, Acetonitrile, and Dimethyl Sulfoxide

“…Reduction potentials that are tabulated following this convention for the hydrogen electrode are referred to as standard reduction potentials. The half-cell reaction for the reduction of a monovalent metal cation is (5) where the symbol "cr" denotes the crystalline phase. Combining the half-cell reactions shown in eqs 4 and 5 leads to the following redox reaction (6) Through the use of thermochemical cycle 1 (illustrated in Scheme 1) and eq 1, the !…”

Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

“…22,23,[44][45][46] In recent years, various approaches have been taken into consideration to realize the function g by more complex machine-learning models such as the neural networks. 24,41,47 NNs, in fact, are powerful data modeling tools able to approximate nonlinear relationships among chemical structural parameters and physical-chemical properties. NNs/ QSPR models for estimating the Henry constant in water were recently reported by English and Carroll.…”

Estimation of infinite dilution activity coefficients of organic compounds in water with neural classifiers

“…However, it is not suitable when pattern recognition or feature extraction capabilities are desired because relationships between variables in such networks are embedded within the weights in a distributed form (Bishop, 1995;Hecht-Nielsen, 1995;Hertz et al, 1991). In difficult problems involving pattern recognition, such as those found in the development of QSPRs for data sets of heterogeneous compound classes, it is advantageous to use neural network classifiers, as shown in a number of recent studies (Espinosa et al, 2000(Espinosa et al, , 2001b(Espinosa et al, , 2002Yaffe et al, 2001Yaffe et al, , 2003 on QSPR development.…”

“…Backpropagation neural networks have recently emerged as an alternative for the development of QSPRs and quantitative structure-activity relationships (QSARs) to predict physicochemical properties and biological activities, respectively (Bünz et al, 1998;Chow et al, 1995;Egolf and Jurs, 1993;Espinosa et al, 2000Espinosa et al, , 2001aGakh et al, 1994;Hall and Story, 1996;Mitchell and Jurs, 1998;Simamoea et al, 1993;Stanton and Jurs, 1990;Stanton et al, 1991;Viswanadhan et al, 2001;Yaffe et al, 2001Yaffe et al, , 2003. This alternative modeling strategy for QSPR development yields significantly higher prediction accuracy compared to that of traditional regressionbased correlations.…”