Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**
作者:Nobuya Tsuji、Pavel Sidorov、Chendan Zhu、Yuuya Nagata、Timur Gimadiev、Alexandre Varnek、Benjamin List
DOI:10.1002/anie.202218659
日期:2023.3.6
Fast and robust predictive models using flexible 2D fragment descriptors, particularly suited for asymmetric catalysis, are described. From training data with only moderate selectivities, highly enantioselective catalysts were predicted and validated, enabling a catalytic asymmetric construction of 2,2-disubstituted tetrahydropyrans.
描述了使用灵活的 2D 片段描述符的快速且稳健的预测模型,特别适用于不对称催化。根据仅具有中等选择性的训练数据,预测并验证了高对映选择性催化剂,从而实现了 2,2-二取代四氢吡喃的催化不对称结构。