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

Yuanyuan Jiang, Zongwei Yang, Jiali Guo, Hongzhen Li, Yijing Liu, Yanzhi Guo, Menglong Li and Xuemei Pu ()
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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

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-26226-7

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