Empowering Molecule Discovery for Molecule-Caption Translation with
  Large Language Models: A ChatGPT Perspective
Jiatong Li Yunqing Liu Wenqi Fan Xiao-Yong Wei Hui Liu Jiliang Tang Qing Li
Molecule discovery plays a crucial role in various scientific fields,
advancing the design of tailored materials and drugs. Traditional methods for
molecule discovery follow a trial-and-error process, which are both
time-consuming and costly, while computational approaches such as artificial
intelligence (AI) have emerged as revolutionary tools to expedite various
tasks, like molecule-caption translation. Despite the importance of
molecule-caption translation for molecule discovery, most of the existing
methods heavily rely on domain experts, require excessive computational cost,
and suffer from poor performance. On the other hand, Large Language Models
(LLMs), like ChatGPT, have shown remarkable performance in various cross-modal
tasks due to their great powerful capabilities in natural language
understanding, generalization, and reasoning, which provides unprecedented
opportunities to advance molecule discovery. To address the above limitations,
in this work, we propose a novel LLMs-based framework (\textbf{MolReGPT}) for
molecule-caption translation, where a retrieval-based prompt paradigm is
introduced to empower molecule discovery with LLMs like ChatGPT without
fine-tuning. More specifically, MolReGPT leverages the principle of molecular
similarity to retrieve similar molecules and their text descriptions from a
local database to ground the generation of LLMs through in-context few-shot
molecule learning. We evaluate the effectiveness of MolReGPT via
molecule-caption translation, which includes molecule understanding and
text-based molecule generation. Experimental results show that MolReGPT
outperforms fine-tuned models like MolT5-base without any additional training.
To the best of our knowledge, MolReGPT is the first work to leverage LLMs in
molecule-caption translation for advancing molecule discovery.