Improving Chemical Understanding of LLMs via SMILES Parsing

Understand

Large language models (LLMs) are increasingly recognized as powerful tools for scientific discovery, particularly in molecular science.

• A fundamental requirement for these models is the ability to accurately understand molecular structures, commonly encoded in the SMILES representation.
• However, current LLMs struggle to interpret SMILES, even failing to carry out basic tasks such as counting molecular rings.
• To address this limitation, we introduce CLEANMOL, a novel framework that formulates SMILES parsing into a suite of clean and deterministic tasks explicitly designed to promote graph-level molecular comprehension.