Large Language Models (LLMs) have demonstrated exceptional performance in
biochemical tasks, especially the molecule caption translation task, which aims
to bridge the gap between molecules and natural language texts. However,
previous methods in adapting LLMs to the molecule-caption translation task
required extra domain-specific pre-training stages, suffered weak alignment
between molecular and textual spaces, or imposed stringent demands on the scale
of LLMs. To resolve the challenges, we propose In-Context Molecule Adaptation
(ICMA), as a new paradigm allowing LLMs to learn the molecule-text alignment
from context examples via In-Context Molecule Tuning. Specifically, ICMA
incorporates the following three stages: Cross-modal Retrieval, Post-retrieval
Re-ranking, and In-context Molecule Tuning. Initially, Cross-modal Retrieval
utilizes BM25 Caption Retrieval and Molecule Graph Retrieval to retrieve
informative context examples. Additionally, we also propose Post-retrieval
Re-ranking with Sequence Reversal and Random Walk to further improve the
quality of retrieval results. Finally, In-Context Molecule Tuning unlocks the
in-context molecule learning capability of LLMs with retrieved examples and
adapts the parameters of LLMs for the molecule-caption translation task.
Experimental results demonstrate that ICMT can empower LLMs to achieve
state-of-the-art or comparable performance without extra training corpora and
intricate structures, showing that LLMs are inherently in-context molecule
learners.

大型语言模型（LLM）在生物化学任务中表现出卓越的性能，特别是分子字幕翻译任务，在分子和自然语言文本之间建立联系。然而，先前的方法在将 LLM 调整到分子 - 字幕翻译任务中需要额外的领域特定预训练阶段，分子和文本空间之间的对齐性较弱，或者对 LLM 的规模有严格要求。为了解决这些挑战，我们提出了一种新的范式：上下文分子适应（ICMA），允许 LLM 通过上下文示例来学习分子 - 文本对应关系。实验证明，ICMT 可以使 LLM 在没有额外的训练语料库和复杂结构的情况下实现最先进的或相当的性能，表明 LLM 本质上是上下文中的分子学习器。

大型语言模型作为上下文化学物质学习器

Large Language Models are In-Context Molecule Learners

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.

本文提出了一种基于大型语言模型的框架（MolReGPT），通过检索式提示范式在上下文中进行少量评估，并利用分子相似性原理从本地数据库检索类似分子及其文本描述来启用 LLMS，用于分子说明翻译以促进分子发现