GLA: Grounding Large Language Models in Molecular Hierarchy for Chemical Understanding

摘要

Conventional Euclidean geometries lead to structural distortion and entangle core pharmacophoric identities with peripheral groups. Existing molecule-language models, relying on linear or uniform encodings, often obscure the hierarchical organization of chemical semantics. To address this, we propose Geometric-Language Alignment (GLA), a framework integrating intrinsic molecular topology into large language models. GLA employs a mixed-curvature encoder that adaptively learns geometric representations through a gating mechanism. These representations are aligned with text via a dual-view contrastive objective and injected into a frozen language model. Experiments on cross-modal retrieval, captioning, and property prediction benchmarks show GLA consistently improves performance over baselines, suggesting that modeling geometric heterogeneity enhances the grounding between molecular structure and chemical language.