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Discovery of Novel Histamine H4 and Serotonin Transporter Ligands Using the Topological Feature Tree Descriptor

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Gedeon Richter Plc, Gyömrői út 19-21, H-1103 Budapest, Hungary
BioSolveIT GmbH, An der Ziegelei 79, 53757 St. Augustin, Germany
*E-mail: [email protected]. Fax: +36-1-4326002. Phone: +36-1-4314605.
Cite this: J. Chem. Inf. Model. 2012, 52, 1, 233–242
Publication Date (Web):December 14, 2011
https://doi.org/10.1021/ci2004972
Copyright © 2011 American Chemical Society

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    Abstract

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    Ligand-based approaches are particularly important in the hit identification process of drug discovery when no structural information on the target is available. Pharmacophore descriptors that use a topological representation of the ligands are usually fast enough to screen large compound libraries effectively when seeking novel lead candidates. One example of this kind is the Feature Tree descriptor, a reduced graph representation implemented in the FTrees software. In this study, we tested the screening efficiency of FTrees by both retrospective and prospective screens using known histamine H4 antagonists and serotonin transporter (SERT) inhibitors as query molecules. Our results demonstrate that FTrees can effectively find actives. Particularly when combined with a subsequent 2D fingerprint-based diversity selection, FTrees was found to be extremely effective at discovering a diverse set of scaffolds. Prospective screening of our in-house compound deck provided several novel H4 and SERT ligands that could serve as suitable starting points for further optimization.

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    Additional material for the retrospective virtual screening. This material is available free of charge via the Internet at http://pubs.acs.org.

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    Cited By

    This article is cited by 7 publications.

    1. Wendy A. Warr, Marc C. Nicklaus, Christos A. Nicolaou, Matthias Rarey. Exploration of Ultralarge Compound Collections for Drug Discovery. Journal of Chemical Information and Modeling 2022, 62 (9) , 2021-2034. https://doi.org/10.1021/acs.jcim.2c00224
    2. Durmus U. Karatay, Jie Zhang, Jeffrey S. Harrison, and David S. Ginger . Classifying Force Spectroscopy of DNA Pulling Measurements Using Supervised and Unsupervised Machine Learning Methods. Journal of Chemical Information and Modeling 2016, 56 (4) , 621-629. https://doi.org/10.1021/acs.jcim.5b00722
    3. Sabine Schultes, Albert J. Kooistra, Henry F. Vischer, Saskia Nijmeijer, Eric E. J. Haaksma, Rob Leurs, Iwan J. P. de Esch, and Chris de Graaf . Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5-HT3A, Histamine H1, and Histamine H4 Receptors. Journal of Chemical Information and Modeling 2015, 55 (5) , 1030-1044. https://doi.org/10.1021/ci500694c
    4. Mari Gabrielsen, Rafał Kurczab, Agata Siwek, Małgorzata Wolak, Aina W. Ravna, Kurt Kristiansen, Irina Kufareva, Ruben Abagyan, Gabriel Nowak, Zdzisław Chilmonczyk, Ingebrigt Sylte, and Andrzej J. Bojarski . Identification of Novel Serotonin Transporter Compounds by Virtual Screening. Journal of Chemical Information and Modeling 2014, 54 (3) , 933-943. https://doi.org/10.1021/ci400742s
    5. Karl‐Heinz Baringhaus, Gerhard Hessler. Virtual Screening. 2015, 251-280. https://doi.org/10.1002/9781118771723.ch9
    6. Wendy A. Warr. Many InChIs and quite some feat. Journal of Computer-Aided Molecular Design 2015, 29 (8) , 681-694. https://doi.org/10.1007/s10822-015-9854-3
    7. Ansgar Schuffenhauer. Computational methods for scaffold hopping. Wiley Interdisciplinary Reviews: Computational Molecular Science 2012, 2 (6) , 842-867. https://doi.org/10.1002/wcms.1106

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