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Global Model for Octanol‐Water Partition Coefficients from Proton Nuclear Magnetic Resonance Spectra
Author(s) -
An Nan,
Van Der Mei Farid,
VoutchkovaKostal Adelina
Publication year - 2014
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201300172
Subject(s) - partition coefficient , partial least squares regression , chemistry , linear regression , octanol , chemical shift , spectral line , proton nmr , nmr spectra database , quantitative structure–activity relationship , regression analysis , biological system , mathematics , analytical chemistry (journal) , statistics , chromatography , stereochemistry , physics , astronomy , biology
The ability to estimate chemical and physical properties from experimental spectra is highly desirable, as it eliminates the need for a priori knowledge of exact chemical structure and allows the property estimation of mixtures. Here we report the proof of principle that a predictive method for octanol‐water partition coefficient (log P ) based on 1 H‐NMR spectra in d 3 ‐chloroform is feasible and can yield accuracy comparable to in silico log P models. The Spectrometric Data‐Activity Relationship (QSDAR) reported predicts log P of neutral organic chemicals using descriptors derived only from 1 H‐NMR chemical shifts, integrations and peak widths. Proton NMR spectra of 140 compounds with diverse structures were used to construct a Multiple Linear Regression (MLR) and a Partial Least Squares (PLS) model that predicts log P. The optimized models were internally validated by K‐fold cross validation and leave‐one‐out validation, and externally with a test set of 28 chemicals. The squared regression coefficients of prediction for the MLR and PLS regression models were 0.970 and 0.971 respectively, showing that the method allows accurate prediction of log P values exclusively from predicted 1 H NMR spectra.