MORN: Molecular Property Prediction Based on Textual-Topological-Spatial Multi-View Learning

Runze Ma, Yidan Zhang, Xinye Wang, Zhenyang Yu, Lei Duan

Abstract: Predicting molecular properties has significant implications for the discovery and generation of drugs and further research in the domain of medicinal chemistry. Learning representations of molecules plays a central role in deep learning-driven property prediction. However, the diversity of molecular features (e.g., chemical system languages, structure notations) brings inconsistency in molecular representation. Moreover, the scarcity of labeled molecular data limits the accuracy of the molecular property prediction model. To address the above issues, we proposed a two-stage method, named MORN, for learning molecular representations for molecular property prediction from a multi-view perspective. In the first stage, textual-topological-spatial multi-views were proposed to learn the molecular representations, so as to capture both chemical system language and structure notation features simultaneously. In the second stage, an adaptive strategy was used to fuse molecular representations learned from multi-views to predict molecular properties. To alleviate the limitation of the scarcity of labeled molecular data, the label restriction was introduced in both multi-view representation learning and fusion stages. The performance of MORN was assessed by seven benchmark molecular datasets and one self-built molecular dataset. Experimental results demonstrated that MORN is effective in molecular property prediction.