让大模型学会用分子结构推理，提升对化学性质的理解能力
Structural Reasoning Improves Molecular Understanding of LLM
- 通过显式引入分子结构特征增强大模型理解能力
- 在已知和未知目标分子场景下均有效提升推理性能
- 适合需要精准化学分析的研究者和药物研发人员
近年来，大语言模型（LLMs）取得显著进展，接近人类感知水平。然而，本研究发现尽管如此，这些模型仍难以利用分子结构信息进行推理。这一差距至关重要，因为许多分子特性（如功能基团）高度依赖于结构细节。为解决此问题，我们提出一种通过草图化分子结构来辅助推理的方法。具体而言，引入分子结构推理（MSR）框架，通过显式融合关键结构特征，提升大模型对分子的理解。我们设计了两种框架，分别适用于目标分子已知或未知的场景。通过大量实验验证，MSR显著改善了模型的分子理解能力。
原文摘要 · Abstract (English)
Recently, large language models (LLMs) have shown significant progress, approaching human perception levels. In this work, we demonstrate that despite these advances, LLMs still struggle to reason using molecular structural information. This gap is critical because many molecular properties, including functional groups, depend heavily on such structural details. To address this limitation, we propose an approach that sketches molecular structures for reasoning. Specifically, we introduce Molecular Structural Reasoning (MSR) framework to enhance the understanding of LLMs by explicitly incorporating the key structural features. We present two frameworks for scenarios where the target molecule is known or unknown. We verify that our MSR improves molecular understanding through extensive experiments.
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