Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**
Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**
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使用可调片段描述符预测高度对映选择性催化剂**
DOI：
10.1002/anie.202218659
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
List Benjamin
中科院分区：
文献类型：
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作者：
Tsuji Nobuya;Sidorov Pavel;Zhu Chendan;Nagata Yuuya;Gimadiev Timur;Varnek Alexandre;List Benjamin
催化剂优化过程通常依赖于化学家基于筛选数据的归纳和定性假设。虽然使用分子特性或计算的3D结构的机器学习模型可以进行定量数据评估，但通常需要昂贵的量子化学计算。相比之下，现成的二进制指纹描述符具有时间和成本效益，但其预测性能仍然不足。在这里，我们描述了一种基于片段描述符的机器学习模型，这些片段描述符针对不对称催化进行了微调，并表示环状或多环芳烃，从而实现了强大而有效的虚拟筛选。使用只有中等选择性的训练数据，我们设计了理论和实验验证的新催化剂，在具有挑战性的不对称四氢吡喃合成中显示出更高的选择性。
Catalyst optimization processes typically rely on inductive and qualitative assumptions of chemists based on screening data. While machine learning models using molecular properties or calculated 3D structures enable quantitative data evaluation, costly quantum chemical calculations are often required. In contrast, readily available binary fingerprint descriptors are time‐ and cost‐efficient, but their predictive performance remains insufficient. Here, we describe a machine learning model based on fragment descriptors, which are fine‐tuned for asymmetric catalysis and represent cyclic or polyaromatic hydrocarbons, enabling robust and efficient virtual screening. Using training data with only moderate selectivities, we designed theoretically and validated experimentally new catalysts showing higher selectivities in a challenging asymmetric tetrahydropyran synthesis.