Chain-of-Thoughts for Molecular Understanding

Abstract

The adaptation of large language models (LLMs) to chemistry have shown promising performance in molecular understanding tasks, such as generating a text description from a molecule. However, proper reasoning based on molecular structural information remains a significant challenge, e.g., even advanced LLMs such as GPT-4o struggle to identify functional groups which are crucial for inferring the molecular property of interest. To address this limitation, we propose \Algname, a structure-aware chain-of-thought (CoT) that enhances LLMs’ understanding of molecular structures by explicitly injecting the key structural features of molecules. Moreover, we introduce two fine-tuning frameworks for adapting the existing LLMs to use our \Algname. Our experiments demonstrate that incorporating \Algname with our fine-tuning frameworks leads to consistent improvements in both molecular understanding tasks.