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Introducing the ‘active search’ method for iterative virtual screening

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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.

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Acknowledgments

Part of this work was supported by the German Science Foundation (DFG) under the reference number GA 1615/1-1.

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Authors and Affiliations

  1. Institute of Computer Science III, Rheinische Friedrich-Wilhelms-Universität Bonn, Römerstr. 164, 53117, Bonn, Germany

    Roman Garnett & Thomas Gärtner

  2. Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven, 53754, Sankt Augustin, Germany

    Thomas Gärtner

  3. Program Unit Chemical Biology and Medicinal Chemistry, Department of Life Science Informatics, B-IT, LIMES, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr. 2, 53113, Bonn, Germany

    Martin Vogt & Jürgen Bajorath

Authors
  1. Roman Garnett
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  2. Thomas Gärtner
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  3. Martin Vogt
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  4. Jürgen Bajorath
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Correspondence to Jürgen Bajorath.

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Garnett, R., Gärtner, T., Vogt, M. et al. Introducing the ‘active search’ method for iterative virtual screening. J Comput Aided Mol Des 29, 305–314 (2015). https://doi.org/10.1007/s10822-015-9832-9

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  • DOI: https://doi.org/10.1007/s10822-015-9832-9

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