Don't Just Encode But See: A Data-Centric Paradigm for Visual Molecular Understanding in Large Language Models

Abstract

Large language models (LLMs) are rapidly becoming interactive scientific assistants. Most existing pipelines rely on specialized molecular encoders and alignment modules, while overlooking the importance of chemical hierarchy and traceable evidence for reliable reasoning and explanation. Thus, we propose MolGlass, a data-centric paradigm for visual molecular understanding in vision-language models (VLMs) that injects chemical priors directly into the visual input through chemical-aware visual augmentations, without modifying model architectures or training molecule-specific encoders. MolGlass enriches depictions with human-interpretable cues such as scaffolds, BRICS-like fragments, and functional groups, with a 2.5D visual encoding strategy that strengthens stereochemical symbols in 2D diagrams to mitigate the gap between 2D and 3D reasoning. Moreover, MolGlass includes a three-stage curriculum (Perceive-Reason-Converse) that progressively trains models from grounding MolGlass conventions to evidence-based reasoning and finally to multi-turn, assistant-style molecular consultation. Across molecular understanding and property prediction tasks, extensive experiments show that MolGlass improves transfer and zero-shot performance and enables visually grounded explanations by tracing model decisions to human-identifiable regions. Our code is available at: https://github.com/RunqingXuCn/MolGlass.