title: Benchmarking Variable Selection in QSAR.
abstract: Variable selection is important in QSAR modeling since it can improve model performance and transparency, as well as reduce the computational cost of model fitting and predictions. Which variable selection methods that perform well in QSAR settings is largely unknown. To address this question we, in a total of 1728 benchmarking experiments, rigorously investigated how eight variable selection methods affect the predictive performance and transparency of random forest models fitted to seven QSAR datasets covering different endpoints, descriptors sets, types of response variables, and number of chemical compounds. The results show that univariate variable selection methods are suboptimal and that the number of variables in the benchmarked datasets can be reduced with about 60 % without significant loss in model performance when using multivariate adaptive regression splines MARS and forward selection.
authors.affiliation: Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, SE-751 24 Uppsala, Sweden. martin.eklund@farmbio.uu.se.
authors.forename: Martin
authors.initials: M
authors.lastname: Eklund
authors.affiliation: AstraZeneca Research and Development, SE-151 85 Södertälje, Sweden.|Department of Pharmacy, Uppsala University, P.O. Box 591, SE-751 23 Uppsala, Sweden.
authors.forename: Ulf
authors.initials: U
authors.lastname: Norinder
authors.affiliation: AstraZeneca Research and Development, SE-431 83 Mölndal, Sweden.
authors.forename: Scott
authors.initials: S
authors.lastname: Boyer
authors.affiliation: AstraZeneca Research and Development, SE-431 83 Mölndal, Sweden.
authors.forename: Lars
authors.initials: L
authors.lastname: Carlsson
keywords: Benchmarking; Model performance; Optimization; Variable selection