AtomDisc通过原子级分词提升分子大模型性能并揭示结构-性质关联
AtomDisc: An Atom-level Tokenizer that Boosts Molecular LLMs and Reveals Structure--Property Associations
- 将原子局部环境量化为结构感知的令牌，嵌入大模型词空间
- 数据驱动识别化学有意义特征，显著提升性质预测与分子生成效果
- 适合追求可解释性与复杂化学推理的分子建模研究者
大语言模型（LLMs）正加速分子科学发现，但将分子信息适配到基于序列的令牌处理仍面临挑战。相比其他表示方式，分子图能显式编码原子连接性和局部拓扑环境，这些是决定原子行为和分子性质的关键因素。尽管已有研究尝试对整体分子拓扑进行分词，但对局部原子环境的细粒度分词仍不充分，而局部环境对复杂化学性质和反应性至关重要。为此，我们提出AtomDisc，一种新框架，将原子级局部环境量化为直接嵌入大模型词空间的结构感知令牌。实验表明，AtomDisc以数据驱动方式可区分具有化学意义的结构特征，揭示结构-性质关联。赋予大模型AtomDisc令牌后，注入了可解释的归纳偏置，在性质预测和分子生成任务中达到当前最优表现。本方法与发现为构建更具机制洞察力和复杂化学推理能力的分子大模型开辟了新路径。
原文摘要 · Abstract (English)
Advances in large language models (LLMs) are accelerating discovery in molecular science. However, adapting molecular information to the serialized, token-based processing of LLMs remains a key challenge. Compared to other representations, molecular graphs explicitly encode atomic connectivity and local topological environments, which are key determinants of atomic behavior and molecular properties. Despite recent efforts to tokenize overall molecular topology, there still lacks effective fine-grained tokenization of local atomic environments, which are critical for determining sophisticated chemical properties and reactivity. To address these issues, we introduce AtomDisc, a novel framework that quantizes atom-level local environments into structure-aware tokens embedded directly in LLM's token space. Our experiments show that AtomDisc, in a data-driven way, can distinguish chemically meaningful structural features that reveal structure-property associations. Equipping LLMs with AtomDisc tokens injects an interpretable inductive bias that delivers state-of-the-art performance on property prediction and molecular generation. Our methodology and findings can pave the way for constructing more powerful molecular LLMs aimed at mechanistic insight and complex chemical reasoning.
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