AtomDisc: 一种提升分子大语言模型并揭示结构-性质关联的原子级分词器
AtomDisc: An Atom-level Tokenizer that Boosts Molecular LLMs and Reveals Structure--Property Associations
AI总结：
AtomDisc通过原子级分词提升分子LLM性能，揭示结构-性质关联。
摘要：
在大语言模型（LLMs）取得进步的背景下，分子科学的发现速度正在加快。然而，将分子信息适应到LLMs的序列化、基于令牌的处理方式仍是一个关键挑战。与其它表示方法相比，分子图明确编码了原子连接性和局部拓扑环境，这些是原子行为和分子性质的关键决定因素。尽管已有努力将整体分子拓扑结构进行分词，但仍然缺乏对局部原子环境的有效细粒度分词，这在确定复杂的化学性质和反应性方面至关重要。为了解决这些问题，我们引入了AtomDisc，一种新的框架，它将原子级别的局部环境量化为结构感知的令牌，并直接嵌入到LLM的令牌空间中。我们的实验表明，AtomDisc以数据驱动的方式能够区分具有化学意义的结构特征，从而揭示结构-性质关联。为LLM配备AtomDisc令牌注入了可解释的归纳偏置，使其在性质预测和分子生成方面实现了最先进的性能。我们的方法和发现可以为构建更强大的分子LLM铺平道路，这些LLM旨在为机理洞察和复杂化学推理服务。
英文摘要：
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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