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  • SPOT-Ligand: Fast and effective structure-based virtual screening by binding homology search according to ligand and receptor similarity

    Author(s)
    Yang, Yuedong
    Zhan, Jian
    Zhou, Yaoqi
    Griffith University Author(s)
    Zhou, Yaoqi
    Zhan, Jian
    Yang, Yuedong
    Year published
    2016
    Metadata
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    Abstract
    Structure‐based virtual screening usually involves docking of a library of chemical compounds onto the functional pocket of the target receptor so as to discover novel classes of ligands. However, the overall success rate remains low and screening a large library is computationally intensive. An alternative to this “ab initio” approach is virtual screening by binding homology search. In this approach, potential ligands are predicted based on similar interaction pairs (similarity in receptors and ligands). SPOT‐Ligand is an approach that integrates ligand similarity by Tanimoto coefficient and receptor similarity by protein ...
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    Structure‐based virtual screening usually involves docking of a library of chemical compounds onto the functional pocket of the target receptor so as to discover novel classes of ligands. However, the overall success rate remains low and screening a large library is computationally intensive. An alternative to this “ab initio” approach is virtual screening by binding homology search. In this approach, potential ligands are predicted based on similar interaction pairs (similarity in receptors and ligands). SPOT‐Ligand is an approach that integrates ligand similarity by Tanimoto coefficient and receptor similarity by protein structure alignment program SPalign. The method was found to yield a consistent performance in DUD and DUD‐E docking benchmarks even if model structures were employed. It improves over docking methods (DOCK6 and AUTODOCK Vina) and has a performance comparable to or better than other binding‐homology methods (FINDsite and PoLi) with higher computational efficiency. The server is available at http://sparks-lab.org.
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    Journal Title
    Journal of Computational Chemistry
    Volume
    37
    Issue
    18
    DOI
    https://doi.org/10.1002/jcc.24380
    Subject
    Physical chemistry
    Physical chemistry not elsewhere classified
    Theoretical and computational chemistry
    Nanotechnology
    Publication URI
    http://hdl.handle.net/10072/100126
    Collection
    • Journal articles

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