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Inverse design of 3d molecular structures with conditional generative neural networks

Author

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  • Niklas W. A. Gebauer

    (Technische Universität Berlin
    Berlin Institute for the Foundations of Learning and Data
    Technische Universität Berlin)

  • Michael Gastegger

    (Technische Universität Berlin
    Technische Universität Berlin)

  • Stefaan S. P. Hessmann

    (Technische Universität Berlin
    Berlin Institute for the Foundations of Learning and Data)

  • Klaus-Robert Müller

    (Technische Universität Berlin
    Berlin Institute for the Foundations of Learning and Data
    Korea University, Anam-dong, Seongbuk-gu
    Max-Planck-Institut für Informatik)

  • Kristof T. Schütt

    (Technische Universität Berlin
    Berlin Institute for the Foundations of Learning and Data)

Abstract

The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a conditional generative neural network for 3d molecular structures with specified chemical and structural properties. This approach is agnostic to chemical bonding and enables targeted sampling of novel molecules from conditional distributions, even in domains where reference calculations are sparse. We demonstrate the utility of our method for inverse design by generating molecules with specified motifs or composition, discovering particularly stable molecules, and jointly targeting multiple electronic properties beyond the training regime.

Suggested Citation

  • Niklas W. A. Gebauer & Michael Gastegger & Stefaan S. P. Hessmann & Klaus-Robert Müller & Kristof T. Schütt, 2022. "Inverse design of 3d molecular structures with conditional generative neural networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28526-y
    DOI: 10.1038/s41467-022-28526-y
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    References listed on IDEAS

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    1. Kathryn Tunyasuvunakool & Jonas Adler & Zachary Wu & Tim Green & Michal Zielinski & Augustin Žídek & Alex Bridgland & Andrew Cowie & Clemens Meyer & Agata Laydon & Sameer Velankar & Gerard J. Kleywegt, 2021. "Highly accurate protein structure prediction for the human proteome," Nature, Nature, vol. 596(7873), pages 590-596, August.
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    Cited by:

    1. Wonho Zhung & Hyeongwoo Kim & Woo Youn Kim, 2024. "3D molecular generative framework for interaction-guided drug design," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Lei Huang & Tingyang Xu & Yang Yu & Peilin Zhao & Xingjian Chen & Jing Han & Zhi Xie & Hailong Li & Wenge Zhong & Ka-Chun Wong & Hengtong Zhang, 2024. "A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. Alessio Fallani & Leonardo Medrano Sandonas & Alexandre Tkatchenko, 2024. "Inverse mapping of quantum properties to structures for chemical space of small organic molecules," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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