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

Mohammad Goodarzi, Richard Jensen, Yvan Vander Heyden*

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

56 Dyfyniadau (Scopus)

Crynodeb

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 (log k(w)). The overall best model was the SVM one built using descriptors selected by ACO. (C) 2012 Elsevier B.V. All rights reserved.

Iaith wreiddiolSaesneg
Tudalennau (o-i)84-94
Nifer y tudalennau11
CyfnodolynJournal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences
Cyfrol910
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Rhag 2012

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