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Prediction of octanol–water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network
Author(s) -
Golmohammadi Hassan
Publication year - 2009
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.21243
Subject(s) - partial least squares regression , partial charge , linear regression , quantitative structure–activity relationship , chemistry , partition coefficient , artificial neural network , biological system , molecule , molecular descriptor , partial correlation , computational chemistry , mathematics , artificial intelligence , statistics , stereochemistry , computer science , correlation , organic chemistry , geometry , biology
Abstract A quantitative structure–property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol–water partition coefficients (log P o/w ). A genetic algorithm was applied as a variable selection tool. Modeling of log P o/w of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA‐3), fractional atomic charge weighted partial positive surface area (FPSA‐3), minimum atomic partial charge ( Q min ), molecular volume (MV), total dipole moment of molecule (μ), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009