title: Automated QSAR with a Hierarchy of Global and Local Models.
abstract: We present an automated QSAR procedure that is used in AstraZeneca's AutoQSAR system. The approach involves automatically selecting the most predictive models from pools of both global and local models. The effectiveness of this QSAR modelling strategy is demonstrated with a retrospective study that uses a diverse selection of 9 early stage AstraZeneca drug discovery projects and 3 physicochemical endpoints: LogD; solubility and human plasma protein binding. We show that the strategy makes a statistically significant improvement to the accuracy of predictions when compared to an updating global strategy, and that the systematic biases inherent in the global model predictions are almost completely removed. This improvement is attributed to the model selection aspect of the strategy.
authors.affiliation: AstraZeneca, Alderley Park, Macclesfield, Cheshire, UK. david.wood@astrazeneca.com.
authors.forename: David J
authors.initials: DJ
authors.lastname: Wood
authors.affiliation: AstraZeneca, Alderley Park, Macclesfield, Cheshire, UK.
authors.forename: David
authors.initials: D
authors.lastname: Buttar
authors.affiliation: AstraZeneca, Alderley Park, Macclesfield, Cheshire, UK.
authors.forename: John G
authors.initials: JG
authors.lastname: Cumming
authors.affiliation: AstraZeneca, Mölndal, Sweden.
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Davis
authors.affiliation: AstraZeneca, Södertälje, Sweden.
authors.forename: Ulf
authors.initials: U
authors.lastname: Norinder
authors.affiliation: Accelrys, Cambridge Science Park, Cambridge, UK.
authors.forename: Sarah L
authors.initials: SL
authors.lastname: Rodgers
keywords: Automated QSAR; Model Selection; QSAR; QSPR