title: Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection.
abstract: Aqueous solubility is recognized as a critical parameter in both the early- and late-stage drug discovery. Therefore, in silico modeling of solubility has attracted extensive interests in recent years. Most previous studies have been limited in using relatively small data sets with limited diversity, which in turn limits the predictability of derived models. In this work, we present a support vector machines model for the binary classification of solubility by taking advantage of the largest known public data set that contains over 46 000 compounds with experimental solubility. Our model was optimized in combination with a reduction and recombination feature selection strategy. The best model demonstrated robust performance in both cross-validation and prediction of two independent test sets, indicating it could be a practical tool to select soluble compounds for screening, purchasing, and synthesizing. Moreover, our work may be used for comparative evaluation of solubility classification studies ascribe to the use of completely public resources.
authors.affiliation: National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA.
authors.forename: Tiejun
authors.initials: T
authors.lastname: Cheng
authors.forename: Qingliang
authors.initials: Q
authors.lastname: Li
authors.forename: Yanli
authors.initials: Y
authors.lastname: Wang
authors.forename: Stephen H
authors.initials: SH
authors.lastname: Bryant
mesh_terms: D001185:Artificial Intelligence; D002965:Classification / Q000379:methods*; D019295:Computational Biology; D055808:Drug Discovery; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000145:classification; D015203:Reproducibility of Results; D012995:Solubility; D014867:Water / Q000737:chemistry*
chemical_list: D004364:Pharmaceutical Preparations; D014867:Water