A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

The
design of molecular catalysts typically involves reconciling
multiple conflicting property requirements, largely relying on human
intuition and local structural searches. However, the vast number
of potential catalysts requires pruning of the candidate space by
efficient property prediction with quantitative structure–property
relationships. Data-driven workflows embedded in a library of potential
catalysts can be used to build predictive models for catalyst performance
and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus­(III)
ligands providing comprehensive physicochemical descriptors based
on representative conformer ensembles. Using quantum-mechanical methods,
we calculated descriptors for 1558 ligands, including commercially
available examples, and trained machine learning models to predict
properties of over 300000 new ligands. We demonstrate the application
of kraken to systematically explore the property
space of organophosphorus ligands and how existing data sets in catalysis
can be used to accelerate ligand selection during reaction optimization.