Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors



Catalyst optimization process is typically relying on an inductive and qualitative assumption 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 previously unaddressed transformation.


Thumbnail image of Manuscript.pdf

Supplementary material

Thumbnail image of SI.pdf
Supporting Information
General information, synthetic procedures, spectral data, computational details, HPLC and GC traces
Thumbnail image of
Modeling data
Screening data, machine learning models, benchmark, and modeling results