Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts.

ABSTRACT: Enantioselective hydrogenation of olefins by Rh-based chiral catalysts has been extensively studied for more than 50 years. Naively, one would expect that everything about this transformation is known and that selecting a catalyst that induces the desired reactivity or selectivity is a trivial task. Nonetheless, ligand engineering or selection for any new prochiral olefin remains an empirical trial-error exercise. In this study, we investigated whether machine learning techniques could be used to accelerate the identification of the most efficient chiral ligand. For this purpose, we used high throughput experimentation to build a large dataset consisting of results for Rh-catalyzed asymmetric olefin hydrogenation, specially designed for applications in machine learning. We showcased its al

SUBMITTER: Kalikadien AV 

PROVIDER: S-EPMC11352728 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature