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A Universal Deep Learning Framework based on Graph Neural Network for Virtual Co-Crystal Screening

submitted on 03.01.2021, 12:03 and posted on 05.01.2021, 04:45 by Yuanyuan Jiang, Jiali Guo, Yjing Liu, Yanzhi Guo, Menglong Li, Xuemei Pu

Cocrystal plays an important role in various fields. However, how to choose coformer remains a challenge on experiments. In this work, we develop a novel graph neural network (GNN) based deep learning framework to rapidly predict formation of the cocrystal. A large and reliable data set is first constructed, which contains 7871 samples. A complementary feature representation is proposed by combining molecular graph and molecular descriptors from priori knowledge. A new GNN learning architecture is then explored to effectively embed the priori knowledge into the “endto-end” learning on the molecular graph, in which multi-head attention mechanism is introduced to further optimize the feature space. Consequently, the performance of our model achieves 98.86% accuracy, greatly surpassing some traditional machine learning models and classic GNN models. Furthermore, the out-of-distribution prediction on energetic cocrystals is also high up to 97.11% accuracy, showing strong generalization.


Email Address of Submitting Author


Sichuan University



ORCID For Submitting Author

Declaration of Conflict of Interest

no conflict of interest