英国利物浦大学Cooper, Andrew I.报道了作为有机分子金属光催化剂配方发现指南的顺序闭环贝叶斯优化策略。相关研究成果发表在2024年6月11日出版的《自然—化学》。

共轭有机光氧化还原催化剂（OPCs）可以促进广泛的化学转化。通过专家知识或使用先验计算，从第一性原理预测OPCs的催化活性是具有挑战性的，因为催化剂活性取决于一系列复杂的相互关联的性质。有机光催化剂和其他催化剂体系通常是通过设计和试错的混合来发现的。

该文中，研究人员报道了一种两步数据驱动的方法，用于OPCs的靶向合成和随后的金属光催化反应优化，证明了氨基酸与芳基卤化物的脱羧sp³–sp²交叉偶联。该方法使用贝叶斯优化策略，结合使用分子描述符对关键物理特性进行编码，从560个候选分子的虚拟库中识别出有前景的OPCs。这导致OPC制剂通过仅探索2.4%的可用催化剂制剂空间（4500种可能的反应条件中的107种）而与铱催化剂具有竞争力。

附：英文原文

Title: Sequential closed-loop Bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery

Author: Li, Xiaobo, Che, Yu, Chen, Linjiang, Liu, Tao, Wang, Kewei, Liu, Lunjie, Yang, Haofan, Pyzer-Knapp, Edward O., Cooper, Andrew I.

Issue&Volume: 2024-06-11

Abstract: Conjugated organic photoredox catalysts (OPCs) can promote a wide range of chemical transformations. It is challenging to predict the catalytic activities of OPCs from first principles, either by expert knowledge or by using a priori calculations, as catalyst activity depends on a complex range of interrelated properties. Organic photocatalysts and other catalyst systems have often been discovered by a mixture of design and trial and error. Here we report a two-step data-driven approach to the targeted synthesis of OPCs and the subsequent reaction optimization for metallophotocatalysis, demonstrated for decarboxylative sp3–sp2 cross-coupling of amino acids with aryl halides. Our approach uses a Bayesian optimization strategy coupled with encoding of key physical properties using molecular descriptors to identify promising OPCs from a virtual library of 560 candidate molecules. This led to OPC formulations that are competitive with iridium catalysts by exploring just 2.4% of the available catalyst formulation space (107 of 4,500 possible reaction conditions).

DOI: 10.1038/s41557-024-01546-5

Source: https://www.nature.com/articles/s41557-024-01546-5