Chain-of-Thoughts for Molecular Understanding
<|reference_start|>Chain-of-Thoughts for Molecular Understanding: The adaptation of large language models (LLMs) to chemistry has 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 StructCoT, 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 StructCoT. Our experiments demonstrate that incorporating StructCoT with our fine-tuning frameworks leads to consistent improvements in both molecular understanding tasks.<|reference_end|>
@article{jang2024chain-of-thoughts,
    title={Chain-of-Thoughts for Molecular Understanding},
    author={Yunhui Jang, Jaehyung Kim, Sungsoo Ahn},
    journal={arXiv preprint arXiv:2410.05610},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05610},
    primaryClass={cs.LG cs.AI}
}