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.

Computational Methods Enable the Prediction of Improved Catalysts for Nickel-Catalyzed Cross-Electrophile Coupling

Cross-electrophile coupling has emerged as an attractive and efficient method for the synthesis of C(sp2)-C(sp3) bonds. These reactions are most often catalyzed by nickel complexes of nitrogenous ligands, especially 2,2'-bipyridines. Precise prediction, selection, and design of optimal ligands remains challenging, despite significant increases in reaction scope and mechanistic understanding. Molecular parameterization and statistical modeling provide a path to the development of improved bipyridine ligands that will enhance the selectivity of existing reactions and broaden the scope of electrophiles that can be coupled. Herein, we describe the generation of a computational ligand library, correlation of observed reaction outcomes with features of the ligands, and the in silico design of improved bipyridine ligands for Ni-catalyzed cross-electrophile coupling. The new nitrogen-substituted ligands display a 5-fold increase in selectivity for product formation versus homodimerization when compared to the current state of the art. This increase in selectivity and yield was general for several cross-electrophile couplings, including the challenging coupling of an aryl chloride with an N-alkylpyridinium salt.

Keeping an "eye" on the experiment: computer vision for real-time monitoring and control

This work presents a generalizable computer vision (CV) and machine learning model that is used for automated real-time monitoring and control of a diverse array of workup processes. Our system simultaneously monitors multiple physical outputs (e.g., liquid level, homogeneity, turbidity, solid, residue, and color), offering a method for rapid data acquisition and deeper analysis from multiple visual cues. We demonstrate a single platform (consisting of CV, machine learning, real-time monitoring techniques, and flexible hardware) to monitor and control vision-based experimental techniques, including solvent exchange distillation, antisolvent crystallization, evaporative crystallization, cooling crystallization, solid-liquid mixing, and liquid-liquid extraction. Both qualitative (video capturing) and quantitative data (visual outputs measurement) were obtained which provided a method for data cross-validation. Our CV model's ease of use, generalizability, and non-invasiveness make it an appealing complementary option to in situ and real-time analytical monitoring tools and mathematical modeling. Additionally, our platform is integrated with Mettler-Toledo's iControl software, which acts as a centralized system for real-time data collection, visualization, and storage. With consistent data representation and infrastructure, we were able to efficiently transfer the technology and reproduce results between different labs. This ability to easily monitor and respond to the dynamic situational changes of the experiments is pivotal to enabling future flexible automation workflows.

Parallel multi-droplet platform for reaction kinetics and optimization

We present an automated droplet reactor platform possessing parallel reactor channels and a scheduling algorithm that orchestrates all of the parallel hardware operations and ensures droplet integrity as well as overall efficiency. We design and incorporate all of the necessary hardware and software to enable the platform to be used to study both thermal and photochemical reactions. We incorporate a Bayesian optimization algorithm into the control software to enable reaction optimization over both categorical and continuous variables. We demonstrate the capabilities of both the preliminary single-channel and parallelized versions of the platform using a series of model thermal and photochemical reactions. We conduct a series of reaction optimization campaigns and demonstrate rapid acquisition of the data necessary to determine reaction kinetics. The platform is flexible in terms of use case: it can be used either to investigate reaction kinetics or to perform reaction optimization over a wide range of chemical domains.

Enabling Suzuki-Miyaura coupling of Lewis-basic arylboronic esters with a nonprecious metal catalyst

The high cost and negative environmental impact of precious metal catalysts has led to increased demand for nonprecious alternatives for widely practiced reactions such as the Suzuki-Miyaura coupling (SMC). Ni-catalyzed versions of this reaction have failed to achieve high reactivity with Lewis-basic arylboron nucleophiles, especially pinacolboron esters. We describe the development of (PPh2Me)2NiCl2 as an inexpensive and air-stable precatalyst that addresses this challenge. Under activation by n-BuMgCl, this complex can catalyze the coupling of synthetically important heteroaryl pinacolborons with heteroaryl halides. Mildly basic conditions (aqueous K3PO4) allow the reaction to tolerate sensitive functional groups that were incompatible with other Ni-SMC methods. Experimental and computational studies suggest that catalyst inhibition by substitution of PPh2Me from Ni(ii) intermediates by Lewis basic reactants and products is disfavored relative to more commonly employed ligands in the Ni-SMC, which allows it to operate efficiently in the presence of Lewis bases such as unhindered pyridines.

Dissecting out the effect of Ru-OAr bonding in a five-coordinate complex of ruthenium (II)

Blue Ru(o-cat)(PPh3)3 (5; o-cat = ortho-catecholate), obtained in 94% yield by reaction of RuCl2(PPh3)3 (4) with dithallium catecholate, was characterized by NMR, MALDI-MS, IR, and single crystal X-ray analysis, and by a combined electronic spectroscopy and time-dependent density functional theory (TD-DFT) study. The frontier orbitals in 5 participate in a low-energy charge transfer excitation involving donation from the Ru-catecholate π bond (largely localized on catecholate) to a low-lying σ* orbital on Ru-PPh3(apical). The energy of this transition increases on binding a pyridine ligand in the sixth site.Key words: ruthenium aryloxide, catecholate, phosphine, electronic spectroscopy, time-dependent DFT.