title: Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods.
abstract: Computational methods for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property are useful for facilitating drug discovery and drug safety evaluation. The quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) methods are the most successfully used statistical learning methods for predicting compounds of specific property. More recently, other statistical learning methods such as neural networks and support vector machines have been explored for predicting compounds of higher structural diversity than those covered by QSAR and QSPR. These methods have shown promising potential in a number of studies. This article is intended to review the strategies, current progresses and underlying difficulties in using statistical learning methods for predicting compounds of specific property. It also evaluates algorithms commonly used for representing structural and physicochemical properties of compounds.
authors.affiliation: Bioinformatics and Drug Design Group, Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543.
authors.forename: C W
authors.initials: CW
authors.lastname: Yap
authors.forename: Y
authors.initials: Y
authors.lastname: Xue
authors.forename: H
authors.initials: H
authors.lastname: Li
authors.forename: Z R
authors.initials: ZR
authors.lastname: Li
authors.forename: C Y
authors.initials: CY
authors.lastname: Ung
authors.forename: L Y
authors.initials: LY
authors.lastname: Han
authors.forename: C J
authors.initials: CJ
authors.lastname: Zheng
authors.forename: Z W
authors.initials: ZW
authors.lastname: Cao
authors.forename: Y Z
authors.initials: YZ
authors.lastname: Chen
mesh_terms: D010599:Pharmacokinetics; D010600:Pharmacology; D021281:Quantitative Structure-Activity Relationship; D014116:Toxicology