Training algorithms to computationally plan multistep organic syntheses has been a challenge for more than 50 years 1 – 7 . However, the field has progressed greatly since the development of early programs such as LHASA 1 , 7 , for which reaction choices at each step were made by human operators. Multiple software platforms 6 , 8 – 14 are now capable of completely autonomous planning. But these programs ‘think’ only one step at a time and have so far been limited to relatively simple targets, the syntheses of which could arguably be designed by human chemists within minutes, without the help of a computer. Furthermore, no algorithm has yet been able to design plausible routes to complex natural products, for which much more far-sighted, multistep planning is necessary 15 , 16 and closely related literature precedents cannot be relied on. Here we demonstrate that such computational synthesis planning is possible, provided that the program’s knowledge of organic chemistry and data-based artificial intelligence routines are augmented with causal relationships 17 , 18 , allowing it to ‘strategize’ over multiple synthetic steps. Using a Turing-like test administered to synthesis experts, we show that the routes designed by such a program are largely indistinguishable from those designed by humans. We also successfully validated three computer-designed syntheses of natural products in the laboratory. Taken together, these results indicate that expert-level automated synthetic planning is feasible, pending continued improvements to the reaction knowledge base and further code optimization. A synthetic route-planning algorithm, augmented with causal relationships that allow it to strategize over multiple steps, can design complex natural-product syntheses that are indistinguishable from those designed by human experts.

中文翻译：

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复杂天然产物合成的计算规划

50 多年来，训练算法以计算规划多步有机合成一直是一项挑战 1 – 7 。然而，自从 LHASA 1 、 7 等早期程序的开发以来，该领域取得了很大进展，其中每个步骤的反应选择都是由人工操作员做出的。多个软件平台 6 , 8 – 14 现在能够完全自主规划。但是这些程序一次只“思考”一个步骤，并且迄今为止仅限于相对简单的目标，可以说，人类化学家可以在几分钟内设计合成，无需计算机的帮助。此外，目前还没有算法能够设计出复杂自然产品的合理路线，为此需要更有远见的多步骤规划 15 、 16 并且不能依赖密切相关的文献先例。在这里，我们证明这种计算合成规划是可能的，前提是程序的有机化学知识和基于数据的人工智能例程通过因果关系 17 、 18 得到增强，使其能够在多个合成步骤上“制定战略”。使用对合成专家进行的类似图灵的测试，我们表明这样一个程序设计的路线与人类设计的路线在很大程度上没有区别。我们还在实验室中成功验证了三种计算机设计的天然产物合成。综上所述，这些结果表明专家级自动化合成规划是可行的，有待于对反应知识库的持续改进和进一步的代码优化。一种合成路线规划算法，