title: Introducing the 'active search' method for iterative virtual screening.
abstract: A method is introduced for sequential similarity searching for active compounds. Given a set of known actives and a screening database, a strategy is devised to optimally rank test compounds by observing the outcome of each iteration before selecting the next compound. This 'active search' approach is based upon Bayesian decision theory. A typical ranking procedure used in virtual compound screening corresponds to a myopic approximation to the optimal strategy. Exploratory active search represents a less-myopic approach and is shown to accurately identify a variety of active compounds in iterative virtual screening trials on 120 compound classes. Source code and data for the active search approach presented herein is made freely available.
authors: Institute of Computer Science III, Rheinische Friedrich-Wilhelms-Universität Bonn, Römerstr. 164, 53117, Bonn, Germany.
authors: Roman
authors: R
authors: Garnett
authors: Thomas
authors: T
authors: Gärtner
authors: Martin
authors: M
authors: Vogt
authors: Jürgen
authors: J
authors: Bajorath
mesh_terms: D001499:Bayes Theorem; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D008024:Ligands
chemical_list: D008024:Ligands
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