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Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials

Author

Listed:
  • Yuanyuan Jiang

    (Sichuan University)

  • Zongwei Yang

    (China Academy of Engineering Physics)

  • Jiali Guo

    (Sichuan University)

  • Hongzhen Li

    (China Academy of Engineering Physics)

  • Yijing Liu

    (Sichuan University)

  • Yanzhi Guo

    (Sichuan University)

  • Menglong Li

    (Sichuan University)

  • Xuemei Pu

    (Sichuan University)

Abstract

Cocrystal engineering have been widely applied in pharmaceutical, chemistry and material fields. However, how to effectively choose coformer has been a challenging task on experiments. Here we develop a graph neural network (GNN) based deep learning framework to quickly predict formation of the cocrystal. In order to capture main driving force to crystallization from 6819 positive and 1052 negative samples reported by experiments, a feasible GNN framework is explored to integrate important prior knowledge into end-to-end learning on the molecular graph. The model is strongly validated against seven competitive models and three challenging independent test sets involving pharmaceutical cocrystals, π–π cocrystals and energetic cocrystals, exhibiting superior performance with accuracy higher than 96%, confirming its robustness and generalization. Furthermore, one new energetic cocrystal predicted is successfully synthesized, showcasing high potential of the model in practice. All the data and source codes are available at https://github.com/Saoge123/ccgnet for aiding cocrystal community.

Suggested Citation

  • Yuanyuan Jiang & Zongwei Yang & Jiali Guo & Hongzhen Li & Yijing Liu & Yanzhi Guo & Menglong Li & Xuemei Pu, 2021. "Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26226-7
    DOI: 10.1038/s41467-021-26226-7
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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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