ChiGNN: Interpretable Algorithm Framework of Molecular Chiral Knowledge-Embedding and Stereosensitive Property Prediction

Molecular chirality-related tasks have remained a notable challenge in materials machine learning (ML) due to the subtle spatial discrepancy between enantiomers. Designing appropriate steric molecular descriptions and embedding chiral knowledge are of great significance for improving the accuracy and interpretability of ML models. In this work, we propose a state-of-the-art deep learning framework, Chiral Graph Neural Network, which can effectively incorporate chiral physicochemical knowledge via Trinity Graph and stereosensitive Message Aggregation encoding. Combined with the quantile regression technique, the accuracy of the chiral chromatographic retention time prediction model outperformed the existing records. Accounting for the inherent merits of this framework, we have customized the Trinity Mask and Contribution Splitting techniques to enable a multilevel interpretation of the model's decision mechanism at atomic, functional group, and molecular hierarchy levels. This interpretation has both scientific and practical implications for the understanding of chiral chromatographic separation and the selection of chromatographic stationary phases. Moreover, the proposed chiral knowledge embedding and interpretable deep learning framework, together with the stereomolecular representation, chiral knowledge embedding method, and multilevel interpretation technique within it, also provide an extensible template and precedent for future chirality-related or stereosensitive ML tasks.