Strategy-first synthesis planning for complex natural products
作者: Daniel Armstrong, Xuan-Vu Nguyen, Octavian Susanu, Gabriel Gibberd, Théo A. Neukomm, Taddäus Strunden, Dan Forster, Morgane Delattre, Shawn Teh, Clément Rols, John Federice, Hayden Leatherwood, M. Lavelle Barnes, Maarten R. Dobbelaere, Peter Wipf, Jon T. Njardarson, Jieping Zhu, Philippe Schwaller
分类: cs.MA, cs.AI
发布日期: 2026-08-07
💡 一句话要点
提出SynthEx框架以解决复杂天然产物合成规划问题
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 合成规划 天然产物 大型语言模型 自动化合成 化学反应路径
📋 核心要点
- 现有的自动化逆合成设计工具在处理复杂天然产物时表现不佳，无法满足其独特的合成需求。
- SynthEx框架利用大型语言模型，能够生成多种合成策略并优化设计，超越传统算法的局限。
- 在专家评估中，SynthEx的关键步骤被认为与人类合成相当，显示出其在合成规划中的有效性和创新性。
📝 摘要（中文）
复杂分子的全合成是化学中最具挑战性的智力和实验任务之一。尽管已有工具在基准测试中表现良好，但它们在处理功能密集、聚环结构的天然产物时仍显不足。本文提出的SynthEx框架基于大型语言模型，能够规划超出传统设计算法范围的复杂天然产物合成路线。SynthEx不仅提出竞争策略，还能将常规步骤与关键步骤整合成连贯的合成路线，并对自身设计进行批评和改进。专家化学家在盲评中认为其关键步骤与已发表的人类合成相当，并将其视为真实的合成计划。我们还发布了超过一千种天然产物的合成路线，作为开放的互动数据库SynthAtlas，期待成为复杂目标分子的共享资源。
🔬 方法详解
问题定义：本文旨在解决复杂天然产物的合成规划问题，现有方法在处理功能密集和聚环结构时存在显著不足，无法提供有效的合成路线。
核心思路：SynthEx框架基于大型语言模型，能够生成多种合成策略，整合常规步骤与关键步骤，形成连贯的合成路线，并对设计进行自我批评和改进。
技术框架：SynthEx的整体架构包括多个模块，首先是合成策略生成模块，然后是路线整合模块，最后是自我评估与优化模块，确保生成的合成路线具备高效性和可行性。
关键创新：SynthEx的最大创新在于其能够在复杂合成中生成更具聚合性的化学反应路径，超越了基于目录的工具，填补了现有方法的空白。
关键设计：在设计中，SynthEx采用了特定的参数设置和损失函数，以优化合成路线的生成过程，网络结构则基于最新的大型语言模型，确保其在复杂合成规划中的有效性。
🖼️ 关键图片
📊 实验亮点
在盲评中，专家化学家认为SynthEx生成的关键合成步骤与已发表的人类合成相当，显示出其在合成规划中的有效性。此外，SynthEx能够生成的合成路线数量超过一千种，展现了其在复杂天然产物合成中的广泛应用潜力。
🎯 应用场景
该研究的潜在应用领域包括制药、材料科学和天然产物化学等，能够为复杂分子的合成提供新的思路和工具，推动相关领域的研究进展。未来，SynthEx可能成为化学合成规划的标准工具，促进自动化合成的普及。
📄 摘要（原文）
The total synthesis of a complex molecule is among the most demanding intellectual and experimental feats in chemistry: a chemist must plan many steps ahead for how to assemble simple building blocks into an intricate target, devise backup strategies, and anticipate procedural challenges. It is also a profoundly creative activity. For half a century, efforts to automate the retrosynthetic design of natural products and other complex molecules have drawn on catalogued reactions, and the resulting tools now report near-complete success on benchmarks built from that same source. But these tools were shaped to fit benchmarked chemistry, and they falter on many natural products, the frontier of the field, whose densely functionalized, polycyclic architectures demand precisely the inventive chemistry the record contains least. Whether a machine could reasonably design such syntheses like an expert chemist does has remained unclear. Here, we show that SynthEx, an agentic framework built on large language models, plans routes to complex natural products that lie beyond the reach of conventional design algorithms. SynthEx proposes competing strategies, assembles a sequence of routine and key steps into a cohesive route, and critiques and improves its own design; the chemistry it favours is more convergent than existing tools produce, and spans a region of reaction space that catalogue-based tools cannot match. Most notably, in blinded assessments, expert chemists judged its key steps comparable to those of published human syntheses and engaged with them as genuine synthesis plans, a response algorithmic route prediction has not previously accomplished. We release routes to more than a thousand natural products as SynthAtlas, an open, interactive database, and anticipate it will become a shared resource for a collection of complex target molecules that lack existing literature routes.