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

Abstract

Quantitative structure-property relationships (QSPRs) for estimating the logarithm octanol/water partition coefficients, logK ow , 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 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 logK ow range of -1.6 to 7.9, with average absolute errors of 0.03 and 0.14 logK ow for the overall data and test sets, respectively. The optimal 12-11-1 back-propagation/QSPR model, for the same range of logK ow , exhibited larger average absolute errors of 0.23 and 0.27 logK ow for the test and validation data sets, respectively, over the same range of logK ow values. The present results with the fuzzy ARTMAP-based QSPR are encouraging and suggest that high performance logK ow QSPR that encompasses a wider range of chemical groups could be developed, following the present approach, by training with a larger heterogeneous data set.

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

“…Recently, Piliszek and colleagues utilized property parameterization model (RM1) and density functional theory (DFT) followed by ANN analysis in prediction of subcooled vapor pressures and classification of 399 polychlorinated trans-azoxybenezenes [91]. Yaffe et al modeled Henry's law constant using both fuzzy ARTMAP and feed forward neural network, the heterogeneous data set (n = 495) included compounds with oxygen, sulphur and nitrogen containing functional groups and halogens [92]. The log Hvalues ranged from 26.72 to 2.87 and topological descriptors were used as input parameters.…”

Artificial neural network models based on QSAR for predicting rejection of neutral organic compounds by polyamide nanofiltration and reverse osmosis membranes

“…Values of log K ow were obtained from KOWWIN [59]. In fact, fundamental chemical parameters are used to predict log K ow , and even neural network-based QSAR methods can produce better results than group-contribution method as has been investigated by Yaffe et al [60]. Nevertheless, the octanol-water partition coefficient was used as empirical descriptor of hydrophobicity because various membrane studies have relied on the use of log K ow as a hydrophobic descriptor [15,18,19,22], and log K ow values are easily accessible.…”

Computational prediction of octanol–water partition coefficient based on the extended solvent-contact model

“…Various quantitative structure-property relationship (QSPR) models with high accuracy were also suggested with the classical [9][10][11] and some novel molecular descriptors such as the semi-empirical electrotopological index [12], intramolecular interactions between functional groups [13], and SMILES-based optimal descriptors [14]. Very recently, Daina et al.…”