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虚拟文库选择全合成二十五种苦恶烷

 2025/1/3 9:49:05 《最新论文》 作者:科学网 小柯机器人 我有话说(0人评论) 字体大小:+

美国斯克利普斯研究所Shenvi Ryan A.团队报道了通过虚拟文库选择实现二十五种苦恶烷的全合成。相关研究成果于2024年12月23日发表在《自然》。

复杂分子的合成从初始设计阶段开始,其中根据与类似反应的类比,通过策略和可行性对可能的路线进行分类。然而,随着分子复杂性的增加,可预测性降低。不可避免的是,即使是经验丰富的化学家也会在获得目标分子的过程中,通过反复试验来鉴定可行的中间体。

该文中,研究人员合成苦恶烷倍半萜时遇到了这样一个问题,其中模式识别方法预期会成功,但结构的微小变化会导致失败。为了解决这个问题,但避免繁琐的猜测和检查实验,研究人员建立了一个难以捉摸的晚期中间体类似物的虚拟库,这些类似物通过反应性进行了分类,并改变了合成途径。该方法导致了25种天然存在的苦恶烷的简洁路线。昂贵的DFT过渡态计算被更快的反应物参数化所取代,以提高可扩展性,并在这种情况下通知机制。

该方法作为人类或计算机辅助合成计划(CASP)的附加搜索,适用于在文献或反应数据库中,几乎没有代表性的高复杂性目标和/或步骤。

附:英文原文

Title: Total synthesis of twenty-five picrotoxanes by virtual library selection

Author: Li, Chunyu, Shenvi, Ryan A.

Issue&Volume: 2024-12-23

Abstract: The synthesis of a complex molecule begins from an initial design stage1,2,3,4 in which possible routes are triaged by strategy and feasibility, based on analogy to similar reactions.2,3 However, as molecular complexity increases, predictability decreases;5 inevitably, even experienced chemists resort to trial-and-error to identify viable intermediates en route to the target molecule. We encountered such a problem in the synthesis of picrotoxane sesquiterpenes in which pattern recognition methods anticipated success, but small variations in structure led to failure. To solve this problem but avoid tedious guess-and-check experimentation, we built a virtual library of elusive late-stage intermediate analogs that were triaged by reactivity and altered the synthesis pathway. The efficiency of this method led to concise routes to twenty-five naturally-occurring picrotoxanes. Costly DFT transition state calculations were replaced with faster reactant parameterizations to increase scalability and, in this case, inform mechanism. This approach served as an add-on search to human or computer-assisted synthesis planning (CASP) applicable to high complexity targets and/or steps with little representation in the literature or reaction databases.

DOI: 10.1038/s41586-024-08538-y

Source: https://www.nature.com/articles/s41586-024-08538-y

来源:科学网  小柯机器人

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