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TamGen: drug design with target-aware molecule generation through a chemical language model

Author

Listed:
  • Kehan Wu

    (University of Science and Technology of China)

  • Yingce Xia

    (Microsoft Research AI for Science)

  • Pan Deng

    (Microsoft Research AI for Science)

  • Renhe Liu

    (Global Health Drug Discovery Institute)

  • Yuan Zhang

    (Global Health Drug Discovery Institute)

  • Han Guo

    (Global Health Drug Discovery Institute)

  • Yumeng Cui

    (Global Health Drug Discovery Institute)

  • Qizhi Pei

    (Renmin University of China)

  • Lijun Wu

    (Microsoft Research AI for Science)

  • Shufang Xie

    (Microsoft Research AI for Science)

  • Si Chen

    (Global Health Drug Discovery Institute)

  • Xi Lu

    (Global Health Drug Discovery Institute)

  • Song Hu

    (Global Health Drug Discovery Institute)

  • Jinzhi Wu

    (Global Health Drug Discovery Institute)

  • Chi-Kin Chan

    (Global Health Drug Discovery Institute)

  • Shawn Chen

    (Global Health Drug Discovery Institute)

  • Liangliang Zhou

    (Global Health Drug Discovery Institute)

  • Nenghai Yu

    (University of Science and Technology of China)

  • Enhong Chen

    (University of Science and Technology of China)

  • Haiguang Liu

    (Microsoft Research AI for Science)

  • Jinjiang Guo

    (Global Health Drug Discovery Institute)

  • Tao Qin

    (Microsoft Research AI for Science)

  • Tie-Yan Liu

    (Microsoft Research AI for Science)

Abstract

Generative drug design facilitates the creation of compounds effective against pathogenic target proteins. This opens up the potential to discover novel compounds within the vast chemical space and fosters the development of innovative therapeutic strategies. However, the practicality of generated molecules is often limited, as many designs focus on a narrow set of drug-related properties, failing to improve the success rate of subsequent drug discovery process. To overcome these challenges, we develop TamGen, a method that employs a GPT-like chemical language model and enables target-aware molecule generation and compound refinement. We demonstrate that the compounds generated by TamGen have improved molecular quality and viability. Additionally, we have integrated TamGen into a drug discovery pipeline and identified 14 compounds showing compelling inhibitory activity against the Tuberculosis ClpP protease, with the most effective compound exhibiting a half maximal inhibitory concentration (IC50) of 1.9 μM. Our findings underscore the practical potential and real-world applicability of generative drug design approaches, paving the way for future advancements in the field.

Suggested Citation

  • Kehan Wu & Yingce Xia & Pan Deng & Renhe Liu & Yuan Zhang & Han Guo & Yumeng Cui & Qizhi Pei & Lijun Wu & Shufang Xie & Si Chen & Xi Lu & Song Hu & Jinzhi Wu & Chi-Kin Chan & Shawn Chen & Liangliang Z, 2024. "TamGen: drug design with target-aware molecule generation through a chemical language model," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53632-4
    DOI: 10.1038/s41467-024-53632-4
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    References listed on IDEAS

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