让大模型直接规划化学合成路线，还能听懂专家指令并保证可行性。
Synthelite: Chemist-aligned and feasibility-aware synthesis planning with LLMs
- 用大模型直接生成逆合成路径，支持自然语言交互调整。
- 在策略和起始物约束下成功率最高达95%。
- 能自动评估反应可行性，适合需要灵活干预的化学家。
计算机辅助合成规划（CASP）长期被视为对合成化学家的有力补充。然而，现有框架缺乏与人类专家交互的机制，限制了化学家经验的融入。本文提出Synthelite，一种基于大语言模型（LLMs）的合成规划框架，可直接生成逆合成转化。Synthelite利用大模型内在的化学知识与推理能力，实现端到端合成路线生成，并通过自然语言提示支持专家干预。实验表明，Synthelite能灵活适应多种用户指定约束，在策略约束和起始物约束任务中均达到最高95%的成功率。此外，该框架具备在路线设计中考虑化学可行性的能力。我们展望Synthelite不仅是实用工具，更是迈向大模型作为合成规划核心协调者的范式转变。
原文摘要 · Abstract (English)
Computer-aided synthesis planning (CASP) has long been envisioned as a complementary tool for synthetic chemists. However, existing frameworks often lack mechanisms to allow interaction with human experts, limiting their ability to integrate chemists' insights. In this work, we introduce Synthelite, a synthesis planning framework that uses large language models (LLMs) to directly propose retrosynthetic transformations. Synthelite can generate end-to-end synthesis routes by harnessing the intrinsic chemical knowledge and reasoning capabilities of LLMs, while allowing expert intervention through natural language prompts. Our experiments demonstrate that Synthelite can flexibly adapt its planning trajectory to diverse user-specified constraints, achieving up to 95\% success rates in both strategy-constrained and starting-material-constrained synthesis tasks. Additionally, Synthelite exhibits the ability to account for chemical feasibility during route design. We envision Synthelite to be both a useful tool and a step toward a paradigm where LLMs are the central orchestrators of synthesis planning.
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