RetroRoute: Route-Level Agentic Decision Making for Multi-Step Retrosynthesis

Development of robust and effective strategies for retrosynthetic planning requires effective decision making throughout of the synthesis process. A critical step in achieving this goal is enabling an agent to evaluate intermediate choices according to their long-term contribution to successful route completion. Current machine learning-based methods often focus on single-step prediction or heuristic search, optimizing locally plausible reactions without explicitly learning how sequential decisions affect the final synthetic route, resulting in suboptimal planning for long and complex syntheses. Therefore, we introduce RetroRoute, an advanced agentic framework for multi-step retrosynthesis that optimizes decisions over the entire synthesis route. The proposed LLM-based REVIEWER and state-aware ANALYZER form the RetroRoute agent for candidate generation and route-level decision making. First, the planning-conditioned REVIEWER generates and reviews candidate reactions based on the target, current intermediate, and search budget, with route-aware utility refinement further identifying chemically feasible and route-compatible alternatives. Furthermore, the state-aware ANALYZER represents each search state with search history and learns to rank candidate reactions toward successful route completion. As a result, RetroRoute optimizes sequential decisions for long-term route utility, enabling agentic global planning toward synthesis completion. In experiments on the USPTO pathways dataset RetroBench, RetroRoute outperforms state-of-the-art methods, achieving up to a 3% improvement in Top-1 test accuracy, particularly for long synthetic routes. These results demonstrate the superiority of RetroRoute in using route-level information to guide agentic decisions over the complete retrosynthetic planning process. They also demonstrate its potential for advancing autonomous retrosynthetic planning and facilitating reliable synthesis route design. Code is available at https://github.com/Su-yq/RetroRoute.