AUTHOR=Liu Xuan , Du Wei , Tang Haibao , Gu Yingjian , Li Zhibang , Fu Xiaoyang 

TITLE=DFusMol: predicting molecular properties based on dual-channel attention

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2025.1623620

DOI=10.3389/fmolb.2025.1623620

ISSN=2296-889X

ABSTRACT=Accurate molecular property prediction is fundamental to modern drug discovery and materials design. However, prevailing computational methods are often insufficient, as they rely on single-granularity structural representations that fail to capture the hierarchical complexity of molecular systems. To address this challenge, we propose a new approach to molecular representation learning that incorporates structural information across multiple scales. We design DFusMol (Dual Fusion with Global and Local Attention), a novel framework inspired by multi-modal learning. DFusMol employs graph encoders to capture features from both atomic-level molecular graphs and motif-level graphs derived from chemical rules. A customized global-local attention mechanism then blends these diverse features to build comprehensive molecular representations. Experiments on nine public benchmark datasets reveal that DFusMol delivers top-tier predictive performance across all tasks, outperforming state-of-the-art self-supervised learning models on six of them. By effectively integrating atomic- and motif-level information, DFusMol provides an innovative and efficient solution for molecular property prediction, enhancing representation learning methodologies and demonstrating strong potential for applications in drug design and lead compound screening.