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Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations
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    Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations
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    • Biao Ma
      Biao Ma
      Center for Cluster Development and Coordination, Foundation for Biomedical Research and Innovation at Kobe, 1-5-2, Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
      RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
      More by Biao Ma
    • Kei Terayama*
      Kei Terayama
      Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
      Graduate School of Medical Life Science, Yokohama City University, 1-7-29, Suehiro-cho, Tsurumi-ku, Yohohama, Kanagawa 230-0045, Japan
      *Email: [email protected]
      More by Kei Terayama
    • Shigeyuki Matsumoto
      Shigeyuki Matsumoto
      Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
    • Yuta Isaka
      Yuta Isaka
      Center for Cluster Development and Coordination, Foundation for Biomedical Research and Innovation at Kobe, 1-5-2, Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
      RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
      More by Yuta Isaka
    • Yoko Sasakura
      Yoko Sasakura
      Center for Cluster Development and Coordination, Foundation for Biomedical Research and Innovation at Kobe, 1-5-2, Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
      RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
    • Hiroaki Iwata
      Hiroaki Iwata
      Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
    • Mitsugu Araki
      Mitsugu Araki
      Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
    • Yasushi Okuno*
      Yasushi Okuno
      Center for Cluster Development and Coordination, Foundation for Biomedical Research and Innovation at Kobe, 1-5-2, Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
      RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
      Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan
      *Email: [email protected]
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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2021, 61, 7, 3304–3313
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    https://doi.org/10.1021/acs.jcim.1c00679
    Published July 9, 2021
    Copyright © 2021 American Chemical Society

    Abstract

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    Recently, molecular generation models based on deep learning have attracted significant attention in drug discovery. However, most existing molecular generation models have serious limitations in the context of drug design wherein they do not sufficiently consider the effect of the three-dimensional (3D) structure of the target protein in the generation process. In this study, we developed a new deep learning-based molecular generator, SBMolGen, that integrates a recurrent neural network, a Monte Carlo tree search, and docking simulations. The results of an evaluation using four target proteins (two kinases and two G protein-coupled receptors) showed that the generated molecules had a better binding affinity score (docking score) than the known active compounds, and the generated molecules possessed a broader chemical space distribution. SBMolGen not only generates novel binding active molecules but also presents 3D docking poses with target proteins, which will be useful in subsequent drug design. The code is available at https://github.com/clinfo/SBMolGen.

    Copyright © 2021 American Chemical Society

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.1c00679.

    • Number of generated molecules, example of generated molecules for target EGFR at C = 1.0, example of generated molecules for target AA2AR at C = 1.0, example of generated molecules for target ADRB2 at C = 1.0; distribution of the similarity score of the generated molecules and distribution of the scaffold similarity score of the generated molecules; distribution of the SA scores of the generated molecules; distributions of the QED scores of the generated molecules; plots of IFIEsum against experimentally determined ΔG for (A) nine CDK2 inhibitors and (B) generated molecules; distribution of the docking scores of the generated molecules against DDR1; chemical structures of nine CDK2 inhibitors and their properties (PDF)

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    Journal of Chemical Information and Modeling

    Cite this: J. Chem. Inf. Model. 2021, 61, 7, 3304–3313
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jcim.1c00679
    Published July 9, 2021
    Copyright © 2021 American Chemical Society

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