arXiv:2607.03007v1 Announce Type: cross Abstract: Recent advances in molecular large language models have led to strong performance on molecular understanding and generation tasks, yet these gains often come without reliable structural grounding. In particular, existing approaches conflict with the chemistry principle that structure determines function: despite their downstream success, current molecular LLMs perform poorly on basic structure recognition, suggesting that they fail to capture molecular graphs from canonical SMILES. To remedy this, we propose MolBasic, a structure-first framewor
The rapid advancement of molecular large language models (LLMs) has revealed fundamental shortcomings in their underlying molecular understanding, necessitating new approaches like MolBasic to address structural grounding.
Improving the fundamental structural understanding in molecular LLMs is crucial for reliable performance in drug discovery, materials science, and synthetic biology, moving beyond superficial pattern recognition.
Molecular LLMs will shift from purely statistical association towards more robust, structure-aware representations, leading to more accurate and trustworthy predictions and generative capabilities.
- · Pharmaceutical R&D
- · Biotechnology companies
- · AI-driven drug discovery platforms
- · Chemical engineering
- · Molecular LLMs lacking structural grounding
- · Purely data-driven molecular modeling without chemical principles
More accurate and efficient design of novel molecules and materials by AI.
Accelerated discovery cycles in drug development and potentially a reduction in R&D costs.
New classes of biomaterials and therapeutics emerge faster, impacting global health and sustainability.
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Read at arXiv cs.AI