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    Automated Recycling of Chemistry for Virtual Screening and Library Design
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    ‡ †Discovery Sciences Computational Sciences and ‡R&I iMed, AstraZeneca R&D, Pepparedsleden 1, S-43183 Mölndal, Sweden
    *E-mail: [email protected]
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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2012, 52, 7, 1777–1786
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    https://doi.org/10.1021/ci300157m
    Published June 3, 2012
    Copyright © 2012 American Chemical Society
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    Abstract

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    An early stage drug discovery project needs to identify a number of chemically diverse and attractive compounds. These hit compounds are typically found through high-throughput screening campaigns. The diversity of the chemical libraries used in screening is therefore important. In this study, we describe a virtual high-throughput screening system called Virtual Library. The system automatically “recycles” validated synthetic protocols and available starting materials to generate a large number of virtual compound libraries, and allows for fast searches in the generated libraries using a 2D fingerprint based screening method. Virtual Library links the returned virtual hit compounds back to experimental protocols to quickly assess the synthetic accessibility of the hits. The system can be used as an idea generator for library design to enrich the screening collection and to explore the structure–activity landscape around a specific active compound.

    Copyright © 2012 American Chemical Society

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    Cited By

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    This article is cited by 17 publications.

    1. Dušan Petrović, James S. Scott, Michael S. Bodnarchuk, Olivier Lorthioir, Scott Boyd, George M. Hughes, Jordan Lane, Allan Wu, David Hargreaves, James Robinson, Jens Sadowski. Virtual Screening in the Cloud Identifies Potent and Selective ROS1 Kinase Inhibitors. Journal of Chemical Information and Modeling 2022, 62 (16) , 3832-3843. https://doi.org/10.1021/acs.jcim.2c00644
    2. Christoph Grebner, Erik Malmerberg, Andrew Shewmaker, Jose Batista, Anthony Nicholls, Jens Sadowski. Virtual Screening in the Cloud: How Big Is Big Enough?. Journal of Chemical Information and Modeling 2020, 60 (9) , 4274-4282. https://doi.org/10.1021/acs.jcim.9b00779
    3. Eric Valeur, Patrick Jimonet. New Modalities, Technologies, and Partnerships in Probe and Lead Generation: Enabling a Mode-of-Action Centric Paradigm. Journal of Medicinal Chemistry 2018, 61 (20) , 9004-9029. https://doi.org/10.1021/acs.jmedchem.8b00378
    4. Joshua Pottel and Nicolas Moitessier . Customizable Generation of Synthetically Accessible, Local Chemical Subspaces. Journal of Chemical Information and Modeling 2017, 57 (3) , 454-467. https://doi.org/10.1021/acs.jcim.6b00648
    5. Christos A. Nicolaou, Ian A. Watson, Hong Hu, and Jibo Wang . The Proximal Lilly Collection: Mapping, Exploring and Exploiting Feasible Chemical Space. Journal of Chemical Information and Modeling 2016, 56 (7) , 1253-1266. https://doi.org/10.1021/acs.jcim.6b00173
    6. Philipp Thiel, Lisa Sach-Peltason, Christian Ottmann, and Oliver Kohlbacher . Blocked Inverted Indices for Exact Clustering of Large Chemical Spaces. Journal of Chemical Information and Modeling 2014, 54 (9) , 2395-2401. https://doi.org/10.1021/ci500150t
    7. Mikko J. Vainio, Thierry Kogej, Florian Raubacher, and Jens Sadowski . Scaffold Hopping by Fragment Replacement. Journal of Chemical Information and Modeling 2013, 53 (7) , 1825-1835. https://doi.org/10.1021/ci4001019
    8. Tim Knehans, Nicholas A. Boyles, Pieter H. Bos. Enumerable Libraries and Accessible Chemical Space in Drug Discovery. 2024, 315-336. https://doi.org/10.1002/9783527840748.ch14
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    10. Han‐Wen Pei, Aatto Laaksonen. Feature vector clustering molecular pairs in computer simulations. Journal of Computational Chemistry 2019, 40 (29) , 2539-2549. https://doi.org/10.1002/jcc.26028
    11. Igor V Tetko, Ola Engkvist, Hongming Chen. Does ‘Big Data’ Exist in Medicinal Chemistry, and If So, How can It Be Harnessed?. Future Medicinal Chemistry 2016, 8 (15) , 1801-1806. https://doi.org/10.4155/fmc-2016-0163
    12. Bernd Wellenzohn, Alexander Weber. Rational Hit Generation. 2016, 159-182. https://doi.org/10.1002/9783527677047.ch07
    13. William R. Pitt, Boris Kroeplien. Exploring Virtual Scaffold Spaces. 2013, 83-104. https://doi.org/10.1002/9783527665143.ch05
    14. Markus Hartenfeller, Steffen Renner, Edgar Jacoby. Reaction‐Driven De Novo Design: a Keystone for Automated Design of Target Family‐Oriented Libraries. 2013, 245-266. https://doi.org/10.1002/9783527677016.ch10
    15. Uta Lessel. Fragment‐Based Design of Focused Compound Libraries. 2013, 349-371. https://doi.org/10.1002/9783527677016.ch15
    16. Giulio Rastelli. Emerging Topics in Structure-Based Virtual Screening. Pharmaceutical Research 2013, 30 (5) , 1458-1463. https://doi.org/10.1007/s11095-013-1012-9
    17. Manish Sud, Eoin Fahy, Shankar Subramaniam. Template-based combinatorial enumeration of virtual compound libraries for lipids. Journal of Cheminformatics 2012, 4 (1) https://doi.org/10.1186/1758-2946-4-23
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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2012, 52, 7, 1777–1786
    Click to copy citationCitation copied!
    https://doi.org/10.1021/ci300157m
    Published June 3, 2012
    Copyright © 2012 American Chemical Society
    Request reuse permissions

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