Boosting the performance of molecular property prediction via graph–text alignment and multi-granularity representation enhancement

• Published in: Journal of molecular graphics & modelling Vol. 132; p. 108843
• Main Authors: Zhao, Zhuoran, Zhou, Qing, Wu, Chengkai, Su, Renbin, Xiong, Weihong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-11-2024
• Subjects: Algorithms; Align; Deep Learning; Models, Molecular; Molecular property prediction; Multi-granularity; Multimodal; Deep learning; Multi-granularity; Align; Molecular property prediction; Multimodal
• ISSN: 1093-3263; 1873-4243; 1873-4243
• DOI: 10.1016/j.jmgm.2024.108843

Deep learning is playing an increasingly important role in accurate prediction of molecular properties. Prior to being processed by a deep learning model, a molecule is typically represented in the form of a text or a graph. While some methods attempt to integrate these two forms of molecular representations, the misalignment of graph and text embeddings presents a significant challenge to fuse two modalities. To solve this problem, we propose a method that aligns and fuses graph and text features in the embedding space by using contrastive loss and cross attentions. Additionally, we enhance the molecular representation by incorporating multi-granularity information of molecules on the levels of atoms, functional groups, and molecules. Extensive experiments show that our model outperforms state-of-the-art models in downstream tasks of molecular property prediction, achieving superior performance with less pretraining data. The source codes and data are available at https://github.com/zzr624663649/multimodal_molecular_property.
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