A Fuzzy ARTMAP Based on Quantitative Structure−Property Relationships (QSPRs) for Predicting Aqueous Solubility of Organic Compounds

Abstract

Quantitative structure-property relationships (QSPRs) for estimating aqueous solubility of organic compounds at 25°C were developed based on a fuzzy ARTMAP and a back-propagation neural networks using a heterogeneous set of 515 organic compounds. A set of molecular descriptors, developed from PM3 semiempirical MO-theory and topological descriptors (first-, second-, third-, and fourth-order molecular connectivity indices), were used as input parameters to the neural networks. Quantum chemical input descriptors included average polarizability, dipole moment, resonance energy, exchange energy, electronnuclear attraction energy, and nuclear-nuclear (core-core) repulsion energy. The fuzzy ARTMAP/QSPR correlated aqueous solubility (S, mol/L) for a range of -11.62 to 4.31 logS with average absolute errors of 0.02 and 0.14 logS units for the overall and validation data sets, respectively. The optimal 11-13-1 backpropagation/QSPR model was less accurate, for the same solubility range, and exhibited larger average absolute errors of 0.29 and 0.28 logS units for the overall and validation sets, respectively. The fuzzy ARTMAP-based QSPR approach was shown to be superior to other back-propagation and multiple linear regression/QSPR models for aqueous solubility of organic compounds.

A Fuzzy ARTMAP‐Based Quantitative Structure—Property Relationship (QSPR) for the Henry′s Law Constant of Organic Compounds.

“…Fuzzy ARTMAP Neural Network Systems. The present fuzzy ARTMAP neural network system was recently introduced for developing QSPRs for boiling temperatures, 18 critical properties, 19 aqueous solubilities, 20 and octanol-water partition coefficients. 21 This fuzzy ARTMAP network is the modification introduced by Giralt et al, 27,36 to the original model of Carpenter et al [22][23][24][25][26] Additional information about fuzzy ART and fuzzy ARTMAP systems can be found elsewhere.…”

“…The advantage of NNs, over classical regression analysis methods, is their inherent ability to incorporate nonlinear relationships between chemical structural parameters and physicochemical properties. [16][17][18][19][20][21] Neural network/QSPR models for estimating the Henry's Law constant for a data set of 357 organic compounds (-7.08 e logH e 2.32) have been recently reported by English and Carroll (2001). 16 The above authors reported QSPRs based on 12-4-1 and 10-3-1 backpropagation neural network architectures (trained using 303 compounds) that performed with absolute errors of 0.237 and 0.281 logH units for the test set (54 compounds), respectively.…”

“…Recently, neural network-based QSPR models that are based on the cognitive classifier fuzzy ARTMAP have been proposed for the estimation of boiling temperature, 18,19 critical properties, 19 aqueous solubility, 20 and octanol-water partition coefficients 21 of organics. The approach was shown to be superior to the popular back-propagation neural network approach as well as other statistical QSPR correlations reported in the literature.…”

Predicting Aqueous Solubility of Chlorinated Hydrocarbons by the MCI Approach

“…The first approach [4][5][6][7][8] is to build model from more easily measured physicochemical properties, such as melting point, boiling point, molar volume, partition coefficient, chromatographic retention time, etc. The other method is based on the information from the molecular of the organic chemicals, which can be further divided into two classes, one is group contributions method [9][10][11][12] and the other is QSPR approach [1,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32].…”

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

“…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.…”