将分子图转化为语言模型可理解的符号，实现零微调下的小样本分子预测。
Bridging Molecular Graphs and Large Language Models
- 用多源数据训练图编码器，拉近分子图与文本表征的距离。
- 提出图令牌对齐策略，使分子图在语言模型中激活泛化能力。
- 结合IUPAC名称提示，显著提升分子分类与回归任务性能。
尽管大语言模型具备出色的泛化能力，但其处理分子结构等图数据的能力仍有限。为此，本文提出Graph2Token，一种无需微调语言模型主干即可将图令牌映射到语言模型词汇的方法。首先，利用多源数据（CHEBI、HMDB）构建分子-文本配对数据集，训练图结构编码器，以缩小图与文本表示在特征空间中的距离。随后，提出一种新颖的对齐策略，将图令牌与语言模型令牌关联。为进一步释放语言模型潜力，收集分子IUPAC名称标识符并融入提示中。通过将分子图作为特殊令牌对齐，可激活语言模型在分子少样本学习中的泛化能力。在分子分类与回归任务上的大量实验验证了Graph2Token的有效性。
原文摘要 · Abstract (English)
While Large Language Models (LLMs) have shown exceptional generalization capabilities, their ability to process graph data, such as molecular structures, remains limited. To bridge this gap, this paper proposes Graph2Token, an efficient solution that aligns graph tokens to LLM tokens. The key idea is to represent a graph token with the LLM token vocabulary, without fine-tuning the LLM backbone. To achieve this goal, we first construct a molecule-text paired dataset from multisources, including CHEBI and HMDB, to train a graph structure encoder, which reduces the distance between graphs and texts representations in the feature space. Then, we propose a novel alignment strategy that associates a graph token with LLM tokens. To further unleash the potential of LLMs, we collect molecular IUPAC name identifiers, which are incorporated into the LLM prompts. By aligning molecular graphs as special tokens, we can activate LLM generalization ability to molecular few-shot learning. Extensive experiments on molecular classification and regression tasks demonstrate the effectiveness of our proposed Graph2Token.
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