title: Chemical predictive modelling to improve compound quality.
abstract: The 'quality' of small-molecule drug candidates, encompassing aspects including their potency, selectivity and ADMET (absorption, distribution, metabolism, excretion and toxicity) characteristics, is a key factor influencing the chances of success in clinical trials. Importantly, such characteristics are under the control of chemists during the identification and optimization of lead compounds. Here, we discuss the application of computational methods, particularly quantitative structure-activity relationships (QSARs), in guiding the selection of higher-quality drug candidates, as well as cultural factors that may have affected their use and impact.
authors.affiliation: Chemistry Innovation Centre, Discovery Sciences, AstraZeneca R&D, Alderley Park, Macclesfield SK10 4TG, UK.
authors.forename: John G
authors.initials: JG
authors.lastname: Cumming
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Davis
authors.forename: Sorel
authors.initials: S
authors.lastname: Muresan
authors.forename: Markus
authors.initials: M
authors.lastname: Haeberlein
authors.forename: Hongming
authors.initials: H
authors.lastname: Chen
mesh_terms: D000818:Animals; D004339:Drug Compounding / Q000592:standards*; D005544:Forecasting; D006801:Humans; D008956:Models, Chemical; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations