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Introducing the 'active search' method for iterative virtual screening
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DOI：10.1007/s10822-015-9832-9.png)
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Abstract
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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.
Keywords:
Active search
Iterative virtual screening
Bayesian decision theory
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