title: Quantitative structure-activity relationship methods: perspectives on drug discovery and toxicology.
abstract: Quantitative structure-activity relationships (QSARs) attempt to correlate chemical structure with activity using statistical approaches. The QSAR models are useful for various purposes including the prediction of activities of untested chemicals. Quantitative structure-activity relationships and other related approaches have attracted broad scientific interest, particularly in the pharmaceutical industry for drug discovery and in toxicology and environmental science for risk assessment. An assortment of new QSAR methods have been developed during the past decade, most of them focused on drug discovery. Besides advancing our fundamental knowledge of QSARs, these scientific efforts have stimulated their application in a wider range of disciplines, such as toxicology, where QSARs have not yet gained full appreciation. In this review, we attempt to summarize the status of QSAR with emphasis on illuminating the utility and limitations of QSAR technology. We will first review two-dimensional (2D) QSAR with a discussion of the availability and appropriate selection of molecular descriptors. We will then proceed to describe three-dimensional (3D) QSAR and key issues associated with this technology, then compare the relative suitability of 2D and 3D QSAR for different applications. Given the recent technological advances in biological research for rapid identification of drug targets, we mention several examples in which QSAR approaches are employed in conjunction with improved knowledge of the structure and function of the target receptor. The review will conclude by discussing statistical validation of QSAR models, a topic that has received sparse attention in recent years despite its critical importance.
authors.affiliation: Logicon ROW Sciences, 3900 NCTR Road, MC 910, Jefferson, Arkansas 72079, USA. rperkins@nctr.fda.gov
authors.forename: Roger
authors.initials: R
authors.lastname: Perkins
authors.forename: Hong
authors.initials: H
authors.lastname: Fang
authors.forename: Weida
authors.initials: W
authors.lastname: Tong
authors.forename: William J
authors.initials: WJ
authors.lastname: Welsh
mesh_terms: D015195:Drug Design; D064420:Drug-Related Side Effects and Adverse Reactions; D004785:Environmental Pollutants / Q000633:toxicity*; D005544:Forecasting; D006801:Humans; D019451:Information Management; D008962:Models, Theoretical; D021281:Quantitative Structure-Activity Relationship; D014116:Toxicology / Q000639:trends
chemical_list: D004785:Environmental Pollutants