Synthesis and Suzuki-Miyaura Cross-Coupling of Alkyl Amine-Boranes. A Boryl Radical-Enabled Strategy

Alkyl organoborons are powerful materials for the construction of C(sp3)-C(sp2) bonds, predominantly via Suzuki-Miyaura cross-coupling. These species are generally assembled using 2-electron processes that harness the ability of boron reagents to act as both electrophiles and nucleophiles. Herein, we demonstrate an alternative borylation strategy based on the reactivity of amine-ligated boryl radicals. This process features the use of a carboxylic acid containing amine-ligated borane that acts as boryl radical precursor for photoredox oxidation and decarboxylation. The resulting amine-ligated boryl radical undergoes facile addition to styrenes and imines through radical-polar crossover manifolds. This delivers a new class of sp3-organoborons that are stable solids and do not undergo protodeboronation. These novel materials include unprotected α-amino derivatives that are generally unstable. Crucially, these aliphatic organoboron species can be directly engaged in Suzuki-Miyaura cross-couplings with structurally complex aryl halides. Preliminary studies suggest that they enable slow-release of the corresponding and often difficult to handle alkyl boronic acids.

Paving the road towards automated homogeneous catalyst design

In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.

Activation of fluoride anion as nucleophile in water with data-guided surfactant selection

A principal component surfactant_map was developed for 91 commonly accessible surfactants for use in surfactant-enabled organic reactions in water, an important approach for sustainable chemical processes. This map was built using 22 experimental and theoretical descriptors relevant to the physicochemical nature of these surfactant-enabled reactions, and advanced principal component analysis algorithms. It is comprised of all classes of surfactants, i.e. cationic, anionic, zwitterionic and neutral surfactants, including designer surfactants. The value of this surfactant_map was demonstrated in activating simple inorganic fluoride salts as effective nucleophiles in water, with the right surfactant. This led to the rapid development (screening 13-15 surfactants) of two fluorination reactions for β-bromosulfides and sulfonyl chlorides in water. The latter was demonstrated in generating a sulfonyl fluoride with sufficient purity for direct use in labelling of chymotrypsin, under physiological conditions.

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design

Computational catalyst design requires identification of a metal and ligand that together result in the desired reaction reactivity and/or selectivity. A major impediment to translating computational designs to experiments is evaluating ligands that are likely to be synthesized. Here, we provide a solution to this impediment with our ReaLigands library that contains >30,000 monodentate, bidentate (didentate), tridentate, and larger ligands cultivated by dismantling experimentally reported crystal structures. Individual ligands from mononuclear crystal structures were identified using a modified depth-first search algorithm and charge was assigned using a machine learning model based on quantum-chemical calculated features. In the library, ligands are sorted based on direct ligand-to-metal atomic connections and on denticity. Representative principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analyses were used to analyze several tridentate ligand categories, which revealed both the diversity of ligands and connections between ligand categories. We also demonstrated the utility of this library by implementing it with our building and optimization tools, which resulted in the very rapid generation of barriers for 750 bidentate ligands for Rh-hydride ethylene migratory insertion.

Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands

Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through the optimization of two sequential reactions required in the asymmetric synthesis of an active pharmaceutical ingredient. To accomplish this, a density functional theory-derived database of >550 bisphosphine ligands was constructed, and a designer chemical space mapping technique was established. The protocol used classification methods to identify active catalysts, followed by linear regression to model reaction selectivity. This led to the prediction and validation of significantly improved ligands for all reaction outputs, suggesting a general strategy that can be readily implemented for reaction optimizations where performance is controlled by bisphosphine ligands.

High-throughput computational workflow for ligand discovery in catalysis with the CSD

A novel semi-automated, high-throughput computational workflow for ligand/catalyst discovery based on the Cambridge Structural Database is reported.

OSCAR: an extensive repository of chemically and functionally diverse organocatalysts

The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries, the field of organocatalysis is mostly dominated by case-by-case studies, with a lack of transferable data-driven tools that facilitate both the exploration of a wider range of catalyst space and the optimization of reaction properties. For these reasons, we introduce OSCAR, a repository of 4000 experimentally derived organocatalysts along with their corresponding building blocks and combinatorially enriched structures. We outline the fragment-based approach used for database generation and showcase the chemical diversity, in terms of functions and molecular properties, covered in OSCAR. The structures and corresponding stereoelectronic properties are publicly available (https://archive.materialscloud.org/record/2022.106) and constitute the starting point to build generative and predictive models for organocatalyst performance.

A reactivity model for oxidative addition to palladium enables quantitative predictions for catalytic cross-coupling reactions

Making accurate, quantitative predictions of chemical reactivity based on molecular structure is an unsolved problem in chemical synthesis, particularly for complex molecules. We report an approach to reactivity prediction for...

Seeking the Optimal Descriptor for SN2 Reactions through Statistical Analysis of Density Functional Theory Results

Bimolecular nucleophilic substitution is one of the fundamental reactions in organic chemistry, yet there is still knowledge to be gained on the role of the nucleophile and the substrate. A statistical treatment of over 600 density functional theory (DFT)-computed barriers for bimolecular nucleophilic substitution at methyl derivatives (S_N2@C) leads to the identification of numerical descriptors that best represent the entering and leaving ability of 26 different nucleophiles. The treatment is based on singular value decomposition (SVD) of a matrix of computed energy barriers. The current work represents the extension to a problem of reactivity of the hidden descriptor methodology that we had previously developed for the thermodynamic problem of bond dissociation energies in transition-metal complexes. The analysis of the results shows that a single descriptor is sufficient. This hidden descriptor has different values for nucleophilic and leaving abilities and, contrary to expectation, does not correlate especially well with either frontier molecular orbital descriptors or solvation descriptors. In contrast, it correlates with other thermodynamic and geometric parameters. This statistical procedure can be in principle extended to additional chemical fragments and other reactions.

Trends in computational molecular catalyst design

Computational methods have emerged as a powerful tool to augment traditional experimental molecular catalyst design by providing useful predictions of catalyst performance and decreasing the time needed for catalyst screening. In this perspective, we discuss three approaches for computational molecular catalyst design: (i) the reaction mechanism-based approach that calculates all relevant elementary steps, finds the rate and selectivity determining steps, and ultimately makes predictions on catalyst performance based on kinetic analysis, (ii) the descriptor-based approach where physical/chemical considerations are used to find molecular properties as predictors of catalyst performance, and (iii) the data-driven approach where statistical analysis as well as machine learning (ML) methods are used to obtain relationships between available data/features and catalyst performance. Following an introduction to these approaches, we cover their strengths and weaknesses and highlight some recent key applications. Furthermore, we present an outlook on how the currently applied approaches may evolve in the near future by addressing how recent developments in building automated computational workflows and implementing advanced ML models hold promise for reducing human workload, eliminating human bias, and speeding up computational catalyst design at the same time. Finally, we provide our viewpoint on how some of the challenges associated with the up-and-coming approaches driven by automation and ML may be resolved.

Building a Toolbox for the Analysis and Prediction of Ligand and Catalyst Effects in Organometallic Catalysis

Computers have become closely involved with most aspects of modern life, and these developments are tracked in the chemical sciences. Recent years have seen the integration of computing across chemical research, made possible by investment in equipment, software development, improved networking between researchers, and rapid growth in the application of predictive approaches to chemistry, but also a change of attitude rooted in the successes of computational chemistry-it is now entirely possible to complete research projects where computation and synthesis are cooperative and integrated, and work in synergy to achieve better insights and improved results. It remains our ambition to put computational prediction before experiment, and we have been working toward developing the key ingredients and workflows to achieve this.The ability to precisely tune selectivity along with high catalyst activity make organometallic catalysts using transition metal (TM) centers ideal for high-value-added transformations, and this can make them appealing for industrial applications. However, mechanistic variations of TM-catalyzed reactions across the vast chemical space of different catalysts and substrates are not fully explored, and such an exploration is not feasible with current resources. This can lead to complete synthetic failures when new substrates are used, but more commonly we see outcomes that require further optimization, such as incomplete conversion, insufficient selectivity, or the appearance of unwanted side products. These processes consume time and resources, but the insights and data generated are usually not tied to a broader predictive workflow where experiments test hypotheses quantitatively, reducing their impact.These failures suggest at least a partial deviation of the reaction pathway from that hypothesized, hinting at quite complex mechanistic manifolds for organometallic catalysts that are affected by the combination of input variables. Mechanistic deviation is most likely when challenging multifunctional substrates are being used, and the quest for so-called privileged catalysts is quickly replaced by a need to screen catalyst libraries until a new "best" match between the catalyst and substrate can be identified and the reaction conditions can be optimized. As a community we remain confined to broad interpretations of the substrate scope of new catalysts and focus on small changes based on idealized catalytic cycles rather than working toward a "big data" view of organometallic homogeneous catalysis with routine use of predictive models and transparent data sharing.Databases of DFT-calculated steric and electronic descriptors can be built for such catalysts, and we summarize here how these can be used in the mapping, interpretation, and prediction of catalyst properties and reactivities. Our motivation is to make these databases useful as tools for synthetic chemists so that they challenge and validate quantitative computational approaches. In this Account, we demonstrate their application to different aspects of catalyst design and discovery and their integration with computational mechanistic studies and thus describe the progress of our journey toward truly predictive models in homogeneous organometallic catalysis.