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Macrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery

Author

Listed:
  • Yanyan Diao

    (East China University of Science & Technology)

  • Dandan Liu

    (East China University of Science & Technology)

  • Huan Ge

    (East China University of Science & Technology)

  • Rongrong Zhang

    (East China University of Science & Technology)

  • Kexin Jiang

    (East China University of Science & Technology)

  • Runhui Bao

    (East China University of Science & Technology)

  • Xiaoqian Zhu

    (East China University of Science & Technology)

  • Hongjie Bi

    (East China University of Science & Technology)

  • Wenjie Liao

    (East China University of Science & Technology)

  • Ziqi Chen

    (East China University of Science & Technology)

  • Kai Zhang

    (East China Normal University)

  • Rui Wang

    (East China University of Science & Technology)

  • Lili Zhu

    (East China University of Science & Technology)

  • Zhenjiang Zhao

    (East China University of Science & Technology)

  • Qiaoyu Hu

    (East China Normal University)

  • Honglin Li

    (East China University of Science & Technology
    East China Normal University
    Lingang Laboratory)

Abstract

Interest in macrocycles as potential therapeutic agents has increased rapidly. Macrocyclization of bioactive acyclic molecules provides a potential avenue to yield novel chemical scaffolds, which can contribute to the improvement of the biological activity and physicochemical properties of these molecules. In this study, we propose a computational macrocyclization method based on Transformer architecture (which we name Macformer). Leveraging deep learning, Macformer explores the vast chemical space of macrocyclic analogues of a given acyclic molecule by adding diverse linkers compatible with the acyclic molecule. Macformer can efficiently learn the implicit relationships between acyclic and macrocyclic structures represented as SMILES strings and generate plenty of macrocycles with chemical diversity and structural novelty. In data augmentation scenarios using both internal ChEMBL and external ZINC test datasets, Macformer display excellent performance and generalisability. We showcase the utility of Macformer when combined with molecular docking simulations and wet lab based experimental validation, by applying it to the prospective design of macrocyclic JAK2 inhibitors.

Suggested Citation

  • Yanyan Diao & Dandan Liu & Huan Ge & Rongrong Zhang & Kexin Jiang & Runhui Bao & Xiaoqian Zhu & Hongjie Bi & Wenjie Liao & Ziqi Chen & Kai Zhang & Rui Wang & Lili Zhu & Zhenjiang Zhao & Qiaoyu Hu & Ho, 2023. "Macrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40219-8
    DOI: 10.1038/s41467-023-40219-8
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    References listed on IDEAS

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    1. Oscar Méndez-Lucio & Benoit Baillif & Djork-Arné Clevert & David Rouquié & Joerg Wichard, 2020. "De novo generation of hit-like molecules from gene expression signatures using artificial intelligence," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    2. Anna C. Belkina & Christopher O. Ciccolella & Rina Anno & Richard Halpert & Josef Spidlen & Jennifer E. Snyder-Cappione, 2019. "Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
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    Cited by:

    1. Tianyu Wu & Min Zhou & Jingcheng Zou & Qi Chen & Feng Qian & Jürgen Kurths & Runhui Liu & Yang Tang, 2024. "AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria," Nature Communications, Nature, vol. 15(1), pages 1-22, December.

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