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    Article

    From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design
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    • Miha Skalic
      Miha Skalic
      Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr Aiguader 88, 08003 Barcelona, Spain
      More by Miha Skalic
      Orcidhttp://orcid.org/0000-0003-4143-4609
    • Davide Sabbadin
      Davide Sabbadin
      Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr Aiguader 88, 08003 Barcelona, Spain
      More by Davide Sabbadin
    • Boris Sattarov
      Boris Sattarov
      Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr Aiguader 88, 08003 Barcelona, Spain
      More by Boris Sattarov
    • Simone Sciabola
      Simone Sciabola
      Biogen Chemistry and Molecular Therapeutics, 115 Broadway Street, Cambridge, Massachusetts 02142, United States
      More by Simone Sciabola
    • Gianni De Fabritiis*
      Gianni De Fabritiis
      Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr Aiguader 88, 08003 Barcelona, Spain
      Acellera, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain
      Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
      *E-mail: [email protected]
      More by Gianni De Fabritiis
      Orcidhttp://orcid.org/0000-0003-3913-4877
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    Molecular Pharmaceutics

    Cite this: Mol. Pharmaceutics 2019, 16, 10, 4282–4291
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    https://doi.org/10.1021/acs.molpharmaceut.9b00634
    Published August 22, 2019
    Copyright © 2019 American Chemical Society
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    Abstract

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    Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generative adversarial network to generate, rather than search, diverse three-dimensional ligand shapes complementary to the pocket. Furthermore, we show that the generated molecule shapes can be decoded using a shape-captioning network into a sequence of SMILES enabling directly the structure-based de novo drug design. We evaluate the quality of the method by both structure- (docking) and ligand-based [quantitative structure–activity relationship (QSAR)] virtual screening methods. For both evaluation approaches, we observed enrichment compared to random sampling from initial chemical space of ZINC drug-like compounds.

    Copyright © 2019 American Chemical Society

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.molpharmaceut.9b00634.

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    Molecular Pharmaceutics

    Cite this: Mol. Pharmaceutics 2019, 16, 10, 4282–4291
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    https://doi.org/10.1021/acs.molpharmaceut.9b00634
    Published August 22, 2019
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