Can Large Language Models Empower Molecular Property Prediction?
Chen Qian Huayi Tang Zhirui Yang Hong Liang Yong Liu
Molecular property prediction has gained significant attention due to its
transformative potential in multiple scientific disciplines. Conventionally, a
molecule graph can be represented either as a graph-structured data or a SMILES
text. Recently, the rapid development of Large Language Models (LLMs) has
revolutionized the field of NLP. Although it is natural to utilize LLMs to
assist in understanding molecules represented by SMILES, the exploration of how
LLMs will impact molecular property prediction is still in its early stage. In
this work, we advance towards this objective through two perspectives:
zero/few-shot molecular classification, and using the new explanations
generated by LLMs as representations of molecules. To be specific, we first
prompt LLMs to do in-context molecular classification and evaluate their
performance. After that, we employ LLMs to generate semantically enriched
explanations for the original SMILES and then leverage that to fine-tune a
small-scale LM model for multiple downstream tasks. The experimental results
highlight the superiority of text explanations as molecular representations
across multiple benchmark datasets, and confirm the immense potential of LLMs
in molecular property prediction tasks. Codes are available at
\url{https://github.com/ChnQ/LLM4Mol}.