用大模型提升多步逆合成规划效率，突破传统方法路径搜索瓶颈。
LLM-Augmented Chemical Synthesis and Design Decision Programs
- 构建路径编码方案与层级搜索策略，跳过逐步预测限制。
- 在标准数据集上实现更高成功率，有效覆盖复杂分子的合成路径。
- 适合药物研发与分子设计人员，尤其关注高效合成路线生成者。
逆合成是将目标分子分解为简单前体的一系列合法反应，是有机化学与药物研发的核心。尽管近年机器学习在单步逆合成建模与后续路径搜索方面取得进展，但受限于可能路径的庞大组合空间。与此同时，大语言模型（LLMs）展现出显著的化学知识，暗示其在复杂决策任务中的潜力。本文探索了LLMs是否能成功解决高度约束的多步逆合成规划问题。我们提出一种高效的反应路径编码方案，并引入新的层级路径搜索策略，突破传统的逐步反应物预测范式。通过全面评估，结果表明该LLM增强方法在逆合成规划中表现优异，并可自然扩展至可合成分子设计这一更广泛挑战。
原文摘要 · Abstract (English)
Retrosynthesis, the process of breaking down a target molecule into simpler precursors through a series of valid reactions, stands at the core of organic chemistry and drug development. Although recent machine learning (ML) research has advanced single-step retrosynthetic modeling and subsequent route searches, these solutions remain restricted by the extensive combinatorial space of possible pathways. Concurrently, large language models (LLMs) have exhibited remarkable chemical knowledge, hinting at their potential to tackle complex decision-making tasks in chemistry. In this work, we explore whether LLMs can successfully navigate the highly constrained, multi-step retrosynthesis planning problem. We introduce an efficient scheme for encoding reaction pathways and present a new route-level search strategy, moving beyond the conventional step-by-step reactant prediction. Through comprehensive evaluations, we show that our LLM-augmented approach excels at retrosynthesis planning and extends naturally to the broader challenge of synthesizable molecular design.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。