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

Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies

In the post-Moore's law era, the progress of electronics relies on discovering superior semiconductor materials and optimizing device fabrication. Computational methods, augmented by emerging data-driven strategies, offer a promising alternative to the traditional trial-and-error approach. In this Perspective, we highlight data-driven computational frameworks for enhancing semiconductor discovery and device development by elaborating on their advances in exploring the materials design space, predicting semiconductor properties and optimizing device fabrication, with a concluding discussion on the challenges and opportunities in these areas.

A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target

A catalyst possessing a broad substrate scope, in terms of both turnover and enantioselectivity, is sometimes called "general". Despite their great utility in asymmetric synthesis, truly general catalysts are difficult or expensive to discover via traditional high-throughput screening and are, therefore, rare. Existing computational tools accelerate the evaluation of reaction conditions from a pre-defined set of experiments to identify the most general ones, but cannot generate entirely new catalysts with enhanced substrate breadth. For these reasons, we report an inverse design strategy based on the open-source genetic algorithm NaviCatGA and on the OSCAR database of organocatalysts to simultaneously probe the catalyst and substrate scope and optimize generality as a primary target. We apply this strategy to the Pictet-Spengler condensation, for which we curate a database of 820 reactions, used to train statistical models of selectivity and activity. Starting from OSCAR, we define a combinatorial space of millions of catalyst possibilities, and perform evolutionary experiments on a diverse substrate scope that is representative of the whole chemical space of tetrahydro-β-carboline products. While privileged catalysts emerge, we show how genetic optimization can address the broader question of generality in asymmetric synthesis, extracting structure-performance relationships from the challenging areas of chemical space.

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.

Predicting success in Cu-catalyzed C-N coupling reactions using data science

Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Here, we report an end-to-end data-driven process to effectively predict how structural features of coupling partners and ligands affect Cu-catalyzed C-N coupling reactions. The established workflow underscores the limitations posed by substrates and ligands while also providing a systematic ligand prediction tool that uses probability to assess when a ligand will be successful. This platform is strategically designed to confront the intrinsic unpredictability frequently encountered in synthetic reaction deployment.

Deconvoluting Nonlinear Catalyst-Substrate Effects in the Intramolecular Dirhodium-Catalyzed C-H Insertion of Donor/Donor Carbenes Using Data Science Tools

Interactions between catalysts and substrates can be highly complex and dynamic, often complicating the development of models to either predict or understand such processes. A dirhodium(II)-catalyzed C-H insertion of donor/donor carbenes into 2-alkoxybenzophenone substrates to form benzodihydrofurans was selected as a model system to explore nonlinear methods to achieve a mechanistic understanding. We found that the application of traditional methods of multivariate linear regression (MLR) correlating DFT-derived descriptors of catalysts and substrates leads to poorly performing models. This inspired the introduction of nonlinear descriptor relationships into modeling by applying the sure independence screening and sparsifying operator (SISSO) algorithm. Based on SISSO-generated descriptors, a high-performing MLR model was identified that predicts external validation points well. Mechanistic interpretation was aided by the deconstruction of feature relationships using chemical space maps, decision trees, and linear descriptors. Substrates were found to have a strong dependence on steric effects for determining their innate cyclization selectivity preferences. Catalyst reactive site features can then be matched to product features to tune or override the resultant diastereoselectivity within the substrate-dictated ranges. This case study presents a method for understanding complex interactions often encountered in catalysis by using nonlinear modeling methods and linear deconvolution by pattern recognition.

Charting the Chemical Reaction Space around a Multicomponent Combination: Controlled Access to a Diverse Set of Biologically Relevant Scaffolds

Charting the chemical reaction space around the combination of carbonyls, amines, and isocyanoacetates allows the description of new multicomponent processes leading to a variety of unsaturated imidazolone scaffolds. The resulting compounds display the chromophore of the green fluorescent protein and the core of the natural product coelenterazine. Despite the competitive nature of the pathways involved, general protocols provide selective access to the desired chemotypes. Moreover, we describe unprecedented reactivity at the C-2 position of the imidazolone core to directly afford C, S, and N-derivatives featuring natural products (e.g. leucettamines), potent kinase inhibitors, and fluorescent probes with suitable optical and biological profiles.

Charting the Chemical Reaction Space around a Multicomponent Combination: Controlled Access to a Diverse Set of Biologically Relevant Scaffolds

Charting the chemical reaction space around the combination of carbonyls, amines, and isocyanoacetates allows the description of new multicomponent processes leading to a variety of unsaturated imidazolone scaffolds. The resulting compounds display the chromophore of the green fluorescent protein and the core of the natural product coelenterazine. Despite the competitive nature of the pathways involved, general protocols provide selective access to the desired chemotypes. Moreover, we describe unprecedented reactivity at the C-2 position of the imidazolone core to directly afford C, S, and N-derivatives featuring natural products (e.g. leucettamines), potent kinase inhibitors, and fluorescent probes with suitable optical and biological profiles.

Data Science-Enabled Palladium-Catalyzed Enantioselective Aryl-Carbonylation of Sulfonimidamides

New methods for the general asymmetric synthesis of sulfonimidamides are of great interest due to their applications in medicinal chemistry, agrochemical discovery, and academic research. We report a palladium-catalyzed cross-coupling method for the enantioselective aryl-carbonylation of sulfonimidamides. Using data science techniques, a virtual library of calculated bisphosphine ligand descriptors was used to guide reaction optimization by effectively sampling the catalyst chemical space. The optimized conditions identified using this approach provided the desired product in excellent yield and enantioselectivity. As the next step, a data science-driven strategy was also used to explore a diverse set of aryl and heteroaryl iodides, providing key information about the scope and limitations of the method. Furthermore, we tested a range of racemic sulfonimidamides for compatibility of this coupling partner. The developed method offers a general and efficient strategy for accessing enantioenriched sulfonimidamides, which should facilitate their application in industrial and academic settings.

A substrate descriptor based approach for the prediction and understanding of the regioselectivity in caged catalyzed hydroformylation

The use of data driven tools to predict the selectivity of homogeneous catalysts has received considerable attention in the past years. In these studies often the catalyst structure is varied, but the use of substrate descriptors to rationalize the catalytic outcome is relatively unexplored. To study whether this may be an effective tool, we investigated both an encapsulated and a non-encapsulated rhodium based catalyst in the hydroformylation reaction of 41 terminal alkenes. For the non-encapsulated catalyst, CAT2, the regioselectivity of the acquired substrate scope could be predicted with high accuracy using the Δ13C NMR shift of the alkene carbon atoms as a descriptor (R2 = 0.74) and when combined with a computed intensity of the CC stretch vibration (ICC stretch) the accuracy increased further (R2 = 0.86). In contrast, a substrate descriptor approach with an encapsulated catalyst, CAT1, appeared more challenging indicating a confined space effect. We investigated Sterimol parameters of the substrates as well as computer-aided drug design descriptors of the substrates, but these parameters did not result in a predictive formula. The most accurate substrate descriptor based prediction was made with the Δ13C NMR shift and ICC stretch (R2 = 0.52), suggestive of the involvement of CH-π interactions. To further understand the confined space effect of CAT1, we focused on the subset of 21 allylbenzene derivatives to investigate predictive parameters unique for this subset. These results showed the inclusion of a charge parameter of the aryl ring improved the regioselectivity predictions, which is in agreement with our assessment that noncovalent interactions between the phenyl ring of the cage and the aryl ring of the substrate are relevant for the regioselectivity outcome. However, the correlation is still weak (R2 = 0.36) and as such we are investigating novel parameters that should improve the overall regioselectivity outcome.

Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation

The borylation of aryl and heteroaryl C-H bonds is valuable for the site-selective functionalization of C-H bonds in complex molecules. Iridium catalysts ligated by bipyridine ligands catalyze the borylation of the C-H bond that is most acidic and least sterically hindered in an arene, but predicting the site of borylation in molecules containing multiple arenes is difficult. To address this challenge, we report a hybrid computational model that predicts the Site of Borylation (SoBo) in complex molecules. The SoBo model combines density functional theory, semiempirical quantum mechanics, cheminformatics, linear regression, and machine learning to predict site selectivity and to extrapolate these predictions to new chemical space. Experimental validation of SoBo showed that the model predicts the major site of borylation of pharmaceutical intermediates with higher accuracy than prior machine-learning models or human experts, demonstrating that SoBo will be useful to guide experiments for the borylation of specific C(sp2)-H bonds during pharmaceutical development.

Catalyst-Controlled Enantioselective and Regiodivergent Addition of Aryl Boron Nucleophiles to N-Alkyl Nicotinate Salts

Dihydropyridines are versatile building blocks for the synthesis of pyridines, tetrahydropyridines, and piperidines. Addition of nucleophiles to activated pyridinium salts allows synthesis of 1,2-, 1,4-, or 1,6-dihydropyridines; however, this process often leads to a mixture of constitutional isomers. Catalyst-controlled regioselective addition of nucleophiles to pyridiniums has the potential to solve this problem. Herein, we report that the regioselective addition of boron-based nucleophiles to pyridinium salts can be accomplished by the choice of a Rh catalyst.