Computationally Guided Ligand Discovery from Compound Libraries and Discovery of a New Class of Ligands for Ni-Catalyzed Cross-Electrophile Coupling of Challenging Quinoline Halides

Although
screening technology has heavily impacted the
fields of
metal catalysis and drug discovery, its application to the discovery
of new catalyst classes has been limited. The diversity of on- and
off-cycle pathways, combined with incomplete mechanistic understanding,
means that screens of potential new ligands have thus far been guided
by intuitive analysis of the metal binding potential. This has resulted
in the discovery of new classes of ligands, but the low hit rates
have limited the use of this strategy because large screens require
considerable cost and effort. Here, we demonstrate a method to identify
promising screening directions via simple and scalable computational
and linear regression tools that leads to a substantial improvement
in hit rate, enabling the use of smaller screens to find new ligands.
The application of this approach to a particular example of Ni-catalyzed
cross-electrophile coupling of aryl halides with alkyl halides revealed
a previously overlooked trend: reactions with more electron-poor amidine
ligands result in a higher yield. Focused screens utilizing this trend
were more successful than serendipity-based screening and led to the
discovery of two new types of ligands, pyridyl oxadiazoles and pyridyl
oximes. These ligands are especially effective for couplings of bromo-
and chloroquinolines and isoquinolines, where they are now the state
of the art. The simplicity of these models with parameters derived
from metal-free ligand structures should make this approach scalable
and widely accessible.