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QSPR for LogP using molecules without oxygen and nitrogen and quantum mechanics descriptors
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Journal Article
QSPR for LogP using molecules without oxygen and nitrogen and quantum mechanics descriptors
2015
Overview
We report a quantitative structure–property relationship (QSPR) study regarding LogP. The text includes some comments regarding the influence of diversity of molecules on results of QSPR computation. The calibration (training) set was selected, according to specific molecular diversity/similarity criteria, from certain group of 621 molecules, without oxygen and nitrogen atoms. The calibration set includes 211 molecules (hydrocarbons, halo-derivatives, thioethers, phosphines etc.) having much higher diversity from the point of view of chemical structure, size, shape and lipophilicity. The validation set includes the non-selected 410 molecules. The observed value of LogP is within (0.20, 9.14) range in calibration set and within (1.08, 9.36) range in prediction/validation set. The best QSPR multilinear formula includes three quantum mechanics descriptors and does not include molecular fragments’ descriptors. For calibration set, the predictive power of QSPR is high (r2 = 0.9489; SEE = 0.4117), close to KOWWIN (r2 = 0.9545; SEE = 0.3961) and miLogP (r2 = 0.9477; SEE = 0.4736) results. The predictor ‘conductor-like screening model area’ has largest influence on the value of LogP. The quality of prediction for validation set is good (r2 = 0.9517; SEE = 0.4334), close to result obtained by KOWWIN (r2 = 0.9676, SEE = 0.3286) and miLogP (r2 = 0.9612; SEE = 0.4776).
Publisher
Springer Nature B.V
Subject
