QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions

Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences
Mohammad GoodarziYvan Vander Heyden

Abstract

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO.

Citations

Aug 11, 2016·Frontiers in Molecular Biosciences·Renata BujakMichał J Markuszewski
Apr 8, 2017·Chirality·Emma EvertssonLinda Thunberg
Dec 6, 2017·Schizophrenia Bulletin·Steven MarwahaDaniel Smith
Oct 5, 2017·SAR and QSAR in Environmental Research·A M Al-FakihM Aziz
Feb 6, 2017·Journal of Chromatographic Science·Tatjana Djakovic Sekulic, Adam Smolinski
Apr 27, 2018·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Jinxiang LuoYongqiang Zhang
Oct 14, 2017·Journal of Chemical Information and Modeling·Ruth I J AmosChristopher A Pohl

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