用大模型+搜索树高效规划分子合成路径，省时3-5倍。
AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search
- 将大模型生成的合成路线映射为可搜索的与或树结构
- 在多个基准上达到顶尖性能，复杂分子上效率提升更明显
- 适合药物和材料设计中的多步合成路径探索
逆合成规划能发现目标分子的可行合成路径，在药物发现和材料设计中至关重要。多步逆合成规划因搜索空间指数级增长和推理成本高而面临计算挑战。尽管大语言模型（LLMs）展现出化学推理能力，但其在合成规划中的应用受限于效率与成本。为此，我们提出AOT*框架，通过将大模型生成的合成路径与系统化的与或树搜索相结合，实现逆合成规划的高效化。AOT*将完整合成路线原子级映射到与或树组件中，并设计了数学严谨的奖励分配策略与基于检索的上下文工程，使大模型能高效导航化学空间。在多个合成基准上的实验表明，AOT*在显著提升搜索效率的同时达到当前最优性能。相较于现有基于LLM的方法，AOT*仅需3-5倍更少的迭代次数即可达成竞争性求解率，且在复杂分子目标上效率优势更加突出。
原文摘要 · Abstract (English)
Retrosynthesis planning enables the discovery of viable synthetic routes for target molecules, playing a crucial role in domains like drug discovery and materials design. Multi-step retrosynthetic planning remains computationally challenging due to exponential search spaces and inference costs. While Large Language Models (LLMs) demonstrate chemical reasoning capabilities, their application to synthesis planning faces constraints on efficiency and cost. To address these challenges, we introduce AOT*, a framework that transforms retrosynthetic planning by integrating LLM-generated chemical synthesis pathways with systematic AND-OR tree search. To this end, AOT* atomically maps the generated complete synthesis routes onto AND-OR tree components, with a mathematically sound design of reward assignment strategy and retrieval-based context engineering, thus enabling LLMs to efficiently navigate in the chemical space. Experimental evaluation on multiple synthesis benchmarks demonstrates that AOT* achieves SOTA performance with significantly improved search efficiency. AOT* exhibits competitive solve rates using 3-5$\times$ fewer iterations than existing LLM-based approaches, with the efficiency advantage becoming more pronounced on complex molecular targets.
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