Multi-MoleScale: a multi-scale approach for molecular property prediction with graph contrastive and sequence learning.

ABSTRACT: 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

SUBMITTER: Lou X 

PROVIDER: S-EPMC12797713 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature