A Molecular Stereostructure Descriptor for Asymmetric Catalysis
A Molecular Stereostructure Descriptor Based On Spherical Projection: L.-C. Xu, X. Li, M.-J. Tang, L.-T. Yuan, J.-Y. Zheng, S.-Q. Zhang, X. Hong
Synlett 2020, 31, DOI: 10.1055/s-0040-1705977
Artificial intelligence (AI) and machine learning (ML) are gathering pace in every aspect of daily living and are progressively coming of age also in the field of organic chemistry, for example for designing and executing complex synthetic sequences. Description of molecular stereostructure is critical for achieving reliable machine learning predictions in different areas of asymmetric synthesis. A number of such descriptors have been developed; however, many of them were not specifically designed for applications in asymmetric catalysis. Recently, the group of Professors Shuo-Qing Zhang and Xin Hong from Zhejing University (Hangzhou, P. R. of China) reported the development of a novel descriptor, specific for the title application.
Professor Hong said: “We developed a spherical projection-based molecular descriptor for the featurization of van der Waals surface. This readily available descriptor can capture the molecular stereostructure and will support the machine learning application in chemistry, especially asymmetric synthesis.”