AOT*: Efficient Synthesis Planning via LLM-Empowered AND-OR Tree Search
This addresses efficiency and cost constraints in synthesis planning for domains like drug discovery, representing a novel method for a known bottleneck.
The paper tackled the challenge of multi-step retrosynthesis planning by introducing AOT*, a framework that integrates LLM-generated pathways with AND-OR tree search, achieving state-of-the-art performance with 3-5× fewer iterations than existing LLM-based approaches.
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.