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人工引导的合成规划提示。

Human-guided synthesis planning prompting.

作者信息

Westerlund Annie M, Saigiridharan Lakshidaa, Genheden Samuel

机构信息

Molecular AI, Discovery Sciences, R&D, AstraZeneca 43183 Gothenburg Sweden.

出版信息

Chem Sci. 2025 Jul 14. doi: 10.1039/d5sc00927h.

Abstract

Contemporary multistep retrosynthesis tools such as AiZynthFinder, which are frequently used by chemists, generate solved routes for the majority of target molecules, but do not consider the prior knowledge of the chemist, including specific bonds that should disconnect or remain connected throughout the routes. Such knowledge is for example integral when planning a joint synthesis route for a set of similar molecules where common disconnection sites can be identified across the molecules. Here, we present a novel strategy in AiZynthFinder for human-guided multistep retrosynthesis prompting. This includes a filter for discarding reactions that violate bonds to freeze constraints. Furthermore, we benchmark four possible strategies for breaking selected bonds in the search for synthetic routes, and show that a combination of a disconnection-aware transformer and a multi-objective search generates routes which satisfy bond constraints for more targets in the PaRoutes dataset compared to the standard search (75.57% 54.80%). Finally, we apply the strategy on a set of drug molecules to exemplify a real-world scenario. Our novel approach enables building a short joint synthesis route that satisfies the given bond constraints and covers eight of the ten molecules, demonstrating the added value of incorporating human prior knowledge in synthesis planning.

摘要

当代的多步逆合成工具,如化学家经常使用的AiZynthFinder,能为大多数目标分子生成已解决的合成路线,但没有考虑化学家的先验知识,包括在整个路线中应该断开或保持连接的特定化学键。例如,在为一组相似分子规划联合合成路线时,这种知识是不可或缺的,因为可以在这些分子中识别出共同的断键位点。在此,我们在AiZynthFinder中提出了一种用于人工引导的多步逆合成提示的新策略。这包括一个用于丢弃违反冻结约束化学键的反应的过滤器。此外,我们对在寻找合成路线时打破选定化学键的四种可能策略进行了基准测试,结果表明,与标准搜索相比,断开感知变压器和多目标搜索的组合在PaRoutes数据集中为更多目标生成了满足化学键约束的路线(75.57%对54.80%)。最后,我们将该策略应用于一组药物分子,以举例说明实际场景。我们的新方法能够构建一条满足给定化学键约束并涵盖十个分子中的八个分子的短联合合成路线,证明了在合成规划中纳入人类先验知识的附加价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae5c/12377211/5015c3a8d09f/d5sc00927h-f1.jpg

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