Local-Global Multimodal Contrastive Learning for Molecular Property Prediction

• URL: http://arxiv.org/abs/2601.22610v1
• Date: Fri, 30 Jan 2026 06:06:17 GMT
• Title: Local-Global Multimodal Contrastive Learning for Molecular Property Prediction
• Authors: Xiayu Liu, Zhengyi Lu, Yunhong Liao, Chan Fan, Hou-biao Li,
• Abstract summary: We propose LGM-CL, a local-global multimodal contrastive learning framework that jointly models molecular graphs and textual representations.<n>Experiments on MoleculeNet benchmarks demonstrate that LGM-CL achieves consistent and competitive performance across both classification and regression tasks.
• Score: 0.4349640169711269
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate molecular property prediction requires integrating complementary information from molecular structure and chemical semantics. In this work, we propose LGM-CL, a local-global multimodal contrastive learning framework that jointly models molecular graphs and textual representations derived from SMILES and chemistry-aware augmented texts. Local functional group information and global molecular topology are captured using AttentiveFP and Graph Transformer encoders, respectively, and aligned through self-supervised contrastive learning. In addition, chemically enriched textual descriptions are contrasted with original SMILES to incorporate physicochemical semantics in a task-agnostic manner. During fine-tuning, molecular fingerprints are further integrated via Dual Cross-attention multimodal fusion. Extensive experiments on MoleculeNet benchmarks demonstrate that LGM-CL achieves consistent and competitive performance across both classification and regression tasks, validating the effectiveness of unified local-global and multimodal representation learning.

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