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A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets

Author

Listed:
  • Lei Huang

    (City University of Hong Kong
    Tencent AI Lab)

  • Tingyang Xu

    (Tencent AI Lab)

  • Yang Yu

    (Tencent AI Lab)

  • Peilin Zhao

    (Tencent AI Lab)

  • Xingjian Chen

    (Harvard Medical School)

  • Jing Han

    (Regor Therapeutics Group)

  • Zhi Xie

    (Regor Therapeutics Group)

  • Hailong Li

    (Regor Therapeutics Group)

  • Wenge Zhong

    (Regor Therapeutics Group)

  • Ka-Chun Wong

    (City University of Hong Kong)

  • Hengtong Zhang

    (Tencent AI Lab)

Abstract

Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (Mpro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46569-1
    DOI: 10.1038/s41467-024-46569-1
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Xin Chen & Kexin Wang & Jianfang Chen & Chao Wu & Jun Mao & Yuanpeng Song & Yijing Liu & Zhenhua Shao & Xuemei Pu, 2024. "Integrative residue-intuitive machine learning and MD Approach to Unveil Allosteric Site and Mechanism for β2AR," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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