Multi-MoleScale: a multi-scale approach for molecular property prediction with graph contrastive and sequence learning
Xinpo Lou, Jianxiu Cai, Shirley W. I. Siu · Journal of Cheminformatics · 2025
In recent years, machine learning models have shown substantial progress in predicting molecular properties. However, integrating molecular graph structures with sequence information continues to present a significant challenge. In this paper, we introduce Multi-MoleScale, a novel multi-scale framework designed to address this challenge. By combining Graph Contrastive Learning (GCL) with sequence-based models like BERT, Multi-MoleScale enhances the prediction of molecular properties by capturing both structural and contextual representations of molecules. Specifically, the model leverages GCL to effectively capture the intrinsic graph-based features of molecules while utilizing BERT’s pretraining capabilities to learn the contextual relationships within molecular sequences. The contrastive learning component enables Multi-MoleScale to distinguish between relevant and irrelevant molecular features, thereby enhancing its predictive accuracy across diverse molecular types. To assess the performance of our method, we conducted experiments on several widely used public datasets, including 12 molecular property datasets, the ADMET dataset, and 14 breast cancer cell line datasets. The results show that Multi-MoleScale consistently outperforms existing deep learning and self-supervised learning approaches. Notably, the model does not require handcrafted features, making it highly adaptable and versatile for a variety of molecular discovery tasks. This makes Multi-MoleScale a promising tool for applications in drug discovery, materials science, and other molecular research fields. Our data and code are available at https://github.com/pdssunny/Multi-MoleScale. We introduce Multi-MoleScale, a novel multi-scale framework that integrates Graph Contrastive Learning (GCL) with sequence-based models such as BERT. This innovative dual approach effectively combines molecular graph structures with sequence information, significantly enhancing predictive accuracy. By capturing both intrinsic graph-based features and contextual relationships within molecular sequences, Multi-MoleScale enables the differentiation between relevant and irrelevant molecular features.