Abstract
Current molecular understanding approaches predominantly focus on the descriptive aspect of human perception, providing broad, topic level insights. However, the referential aspect—linking molecular concepts to specific structural components—remains largely unexplored. To address this gap, we propose a molecular grounding benchmark designed to evaluate a model’s referential abilities. We align molecular grounding with established conventions in NLP, cheminformatics, and molecular science, showcasing the potential of NLP techniques to advance molecular under standing within the AI for Science movement. Furthermore, we constructed the largest molecular understanding benchmark to date, compris ing 79k QA pairs, and developed a multi-agent grounding prototype as proof of concept. This system outperforms existing models, including GPT-4o, and its grounding outputs have been integrated to enhance traditional tasks such as molecular captioning and ATC (Anatomical, Therapeutic, Chemical) classification.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
"MolGround: A Benchmark for Molecular Grounding", Technical Disclosure Commons, (April 28, 2025)
https://www.tdcommons.org/dpubs_series/8043