A priori theoretical prediction of selectivity in asymmetric catalysis: design of chiral catalysts by using quantum molecular interaction fields

Molecular field analysis for data-driven molecular design in asymmetric catalysis

This review highlights the recent advances (2019-present) in the use of MFA (molecular field analysis) for data-driven catalyst design, enabling to improve selectivities/reaction outcomes in asymmetric catalysis. Successful examples of MFA-based molecular design and how to design molecules by MFA are described, including how to generate and evaluate MFA-based regression models, and future challenges in MFA-based molecular design in molecular catalysis.

VCD spectroscopy distinguishes the enamine and iminium ion of a 1,1’-binaphthyl azepine

We present a VCD spectroscopic characterization of a chiral 1,1’-binaphthyl azepine catalyst and show that the VCD spectra of an in-situ generated enamine and an ex-situ prepared iminium ion are...

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.

Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization

The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here, we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene : 1-octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr-N distance, Cr-α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene.

Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

The dawn of the 21st century has brought with it a surge of research related to computer-guided approaches to catalyst design. In the past two decades, chemoinformatics, the application of informatics to solve problems in chemistry, has increasingly influenced prediction of activity and mechanistic investigations of organic reactions. The advent of advanced statistical and machine learning methods, as well as dramatic increases in computational speed and memory, has contributed to this emerging field of study. This review summarizes strategies to employ quantitative structure-selectivity relationships (QSSR) in asymmetric catalytic reactions. The coverage is structured by initially introducing the basic features of these methods. Subsequent topics are discussed according to increasing complexity of molecular representations. As the most applied subfield of QSSR in enantioselective catalysis, the application of local parametrization approaches and linear free energy relationships (LFERs) along with multivariate modeling techniques is described first. This section is followed by a description of global parametrization methods, the first of which is continuous chirality measures (CCM) because it is a single parameter derived from the global structure of a molecule. Chirality codes, global, multivariate descriptors, are then introduced followed by molecular interaction fields (MIFs), a global descriptor class that typically has the highest dimensionality. To highlight the current reach of QSSR in enantioselective transformations, a comprehensive collection of examples is presented. When combined with traditional experimental approaches, chemoinformatics holds great promise to predict new catalyst structures, rationalize mechanistic behavior, and profoundly change the way chemists discover and optimize reactions.

Application of Q2MM to predictions in stereoselective synthesis

Quantum-Guided Molecular Mechanics (Q2MM) can be used to derive transition state force fields (TSFFs) that allow the fast and accurate predictions of stereoselectivity for a wide range of catalytic enantioselective reactions. The basic ideas behind the derivation of TSFFs using Q2MM are discussed and the steps involved in obtaining a TSFF using the Q2MM code, publically available at github.com/q2mm, are shown. The applicability for a range of reactions, including several non-standard applications of Q2MM, is demonstrated. Future developments of the method are also discussed.

Synthesis meets theory: Past, present and future of rational chemistry

Chemical synthesis has its roots in the empirical approach of alchemy. Nonetheless, the birth of the scientific method, the technical and technological advances (exploiting revolutionary discoveries in physics) and the improved management and sharing of growing databases greatly contributed to the evolution of chemistry from an esoteric ground into a mature scientific discipline during these last 400 years. Furthermore, thanks to the evolution of computational resources, platforms and media in the last 40 years, theoretical chemistry has added to the puzzle the final missing tile in the process of “rationalizing” chemistry. The use of mathematical models of chemical properties, behaviors and reactivities is nowadays ubiquitous in literature. Theoretical chemistry has been successful in the difficult task of complementing and explaining synthetic results and providing rigorous insights when these are otherwise unattainable by experiment. The first part of this review walks the reader through a concise historical overview on the evolution of the “model” in chemistry. Salient milestones have been highlighted and briefly discussed. The second part focuses more on the general description of recent state-of-the-art computational techniques currently used worldwide by chemists to produce synergistic models between theory and experiment. Each section is complemented by key-examples taken from the literature that illustrate the application of the technique discussed therein.

A Holy Grail in Chemistry: Computational Catalyst Design: Feasible or Fiction?

Efficient and selective catalysis lies at the heart of much of chemistry, enabling the synthesis of molecules and materials with enormous societal and technological impact. Modern in silico tools should allow us to develop new catalysts faster and better than ever before; this contribution discusses the feasibility and potential of computational catalyst design.

Mini-ISES identifies promising carbafructopyranose-based salens for asymmetric catalysis: Tuning ligand shape via the anomeric effect

This study introduces new methods of screening for and tuning chiral space and in so doing identifies a promising set of chiral ligands for asymmetric synthesis. The carbafructopyranosyl-1,2-diamine(s) and salens constructed therefrom are particularly compelling. It is shown that by removing the native anomeric effect in this ligand family, one can tune chiral ligand shape and improve chiral bias. This concept is demonstrated by a combination of (i) x-ray crystallographic structure determination, (ii) assessment of catalytic performance, and (iii) consideration of the anomeric effect and its underlying dipolar basis. The title ligands were identified by a new mini version of the in situ enzymatic screening (ISES) procedure through which catalyst-ligand combinations are screened in parallel, and information on relative rate and enantioselectivity is obtained in real time, without the need to quench reactions or draw aliquots. Mini-ISES brings the technique into the nanomole regime (200 to 350 nmol catalyst/20 μml organic volume) commensurate with emerging trends in reaction development/process chemistry. The best-performing β-d-carbafructopyranosyl-1,2-diamine-derived salen ligand discovered here outperforms the best known organometallic and enzymatic catalysts for the hydrolytic kinetic resolution of 3-phenylpropylene oxide, one of several substrates examined for which the ligand is "matched." This ligand scaffold defines a new swath of chiral space, and anomeric effect tunability defines a new concept in shaping that chiral space. Both this ligand set and the anomeric shape-tuning concept are expected to find broad application, given the value of chiral 1,2-diamines and salens constructed from these in asymmetric catalysis.

Structure-thermodynamics-antioxidant activity relationships of selected natural phenolic acids and derivatives: an experimental and theoretical evaluation

Phenolic acids and derivatives have potential biological functions, however, little is known about the structure-activity relationships and the underlying action mechanisms of these phenolic acids to date. Herein we investigate the structure-thermodynamics-antioxidant relationships of 20 natural phenolic acids and derivatives using DPPH^• scavenging assay, density functional theory calculations at the B3LYP/6-311++G(d,p) levels of theory, and quantitative structure-activity relationship (QSAR) modeling. Three main working mechanisms (HAT, SETPT and SPLET) are explored in four micro-environments (gas-phase, benzene, water and ethanol). Computed thermodynamics parameters (BDE, IP, PDE, PA and ETE) are compared with the experimental radical scavenging activities against DPPH^•. Available theoretical and experimental investigations have demonstrated that the extended delocalization and intra-molecular hydrogen bonds are the two main contributions to the stability of the radicals. The C = O or C = C in COOH, COOR, C = CCOOH and C = CCOOR groups, and orthodiphenolic functionalities are shown to favorably stabilize the specific radical species to enhance the radical scavenging activities, while the presence of the single OH in the ortho position of the COOH group disfavors the activities. HAT is the thermodynamically preferred mechanism in the gas phase and benzene, whereas SPLET in water and ethanol. Furthermore, our QSAR models robustly represent the structure-activity relationships of these explored compounds in polar media.

Direct asymmetric amination of α-branched cyclic ketones catalyzed by a chiral phosphoric acid

Here we report the direct asymmetric amination of α-substituted cyclic ketones catalyzed by a chiral phosphoric acid, yielding products with a N-containing quaternary stereocenter in high yields and excellent enantioselectivities. Kinetic resolution of the starting ketone was also found to occur on some of the substrates under milder conditions, providing enantioenriched α-branched ketones, another important building block in organic synthesis. The utility of this methodology was demonstrated in the short synthesis of (S)-ketamine, the more active enantiomer of this versatile pharmaceutical.

A computationally designed titanium-mediated amination of allylic alcohols for the synthesis of secondary allylamines

A direct amination on allylic alcohols under mild conditions was enlightened by computational investigations and implemented in secondary allylamines synthesis.

Nanomole-scale high-throughput chemistry for the synthesis of complex molecules

At the forefront of new synthetic endeavors, such as drug discovery or natural product synthesis, large quantities of material are rarely available and timelines are tight. A miniaturized automation platform enabling high-throughput experimentation for synthetic route scouting to identify conditions for preparative reaction scale-up would be a transformative advance. Because automated, miniaturized chemistry is difficult to carry out in the presence of solids or volatile organic solvents, most of the synthetic “toolkit” cannot be readily miniaturized. Using palladium-catalyzed cross-coupling reactions as a test case, we developed automation-friendly reactions to run in dimethyl sulfoxide at room temperature. This advance enabled us to couple the robotics used in biotechnology with emerging mass spectrometry–based high-throughput analysis techniques. More than 1500 chemistry experiments were carried out in less than a day, using as little as 0.02 milligrams of material per reaction.

Designer substrate library for quantitative, predictive modeling of reaction performance

Assessment of reaction substrate scope is often a qualitative endeavor that provides general indications of substrate sensitivity to a measured reaction outcome. Unfortunately, this field standard typically falls short of enabling the quantitative prediction of new substrates' performance. The disconnection between a reaction's development and the quantitative prediction of new substrates' behavior limits the applicative usefulness of many methodologies. Herein, we present a method by which substrate libraries can be systematically developed to enable quantitative modeling of reaction systems and the prediction of new reaction outcomes. Presented in the context of rhodium-catalyzed asymmetric transfer hydrogenation, these models quantify the molecular features that influence enantioselection and, in so doing, lend mechanistic insight to the modes of asymmetric induction.

Computational ligand design for the reductive elimination of ArCF₃ from a small bite angle Pd(II) complex: remarkable effect of a perfluoroalkyl phosphine

To date only three ligands are known to trigger the challenging reductive elimination of ArCF3 from Pd(II). We report the computational design of a bidentate trifluoromethylphosphine ligand that although exhibiting a generally ineffective small bite angle is predicted to give facile reductive elimination. Our experimental verification gave quantitative formation of ArCF3 at 80 °C within 2 h. This highlights the distinct effect of P-CF3 in organometallic reactivity and constitutes a proof-of-principle study of computational reactivity design.

Rational design of catalysts for asymmetric diamination reaction using transition state modeling

DFT calculations have been used to design chiral phosphoramidite ligands for the asymmetric diamination of vicinal diamines. The substituents at both the 3,3′ positions of the binol framework and the amido nitrogen play a vital role in the stereochemical outcome.

Insights into the adsorption of simple benzene derivatives on carbon nanotubes

This work characterizes the adsorption characteristics of simple benzene derivatives on carbon nanotubes.

An index for characterization of natural and non-natural amino acids for peptidomimetics

Bioactive peptides and peptidomimetics play a pivotal role in the regulation of many biological processes such as cellular apoptosis, host defense, and biomineralization. In this work, we develop a novel structural matrix, Index of Natural and Non-natural Amino Acids (NNAAIndex), to systematically characterize a total of 155 physiochemical properties of 22 natural and 593 non-natural amino acids, followed by clustering the structural matrix into 6 representative property patterns including geometric characteristics, H-bond, connectivity, accessible surface area, integy moments index, and volume and shape. As a proof-of-principle, the NNAAIndex, combined with partial least squares regression or linear discriminant analysis, is used to develop different QSAR models for the design of new peptidomimetics using three different peptide datasets, i.e., 48 bitter-tasting dipeptides, 58 angiotensin-converting enzyme inhibitors, and 20 inorganic-binding peptides. A comparative analysis with other QSAR techniques demonstrates that the NNAAIndex method offers a stable and predictive modeling technique for in silico large-scale design of natural and non-natural peptides with desirable bioactivities for a wide range of applications.

A systematic investigation of quaternary ammonium ions as asymmetric phase-transfer catalysts. Application of quantitative structure activity/selectivity relationships

Although the synthetic utility of asymmetric phase-transfer catalysis continues to expand, the number of proven catalyst types and design criteria remains limited. At the origin of this scarcity is a lack in understanding of how catalyst structural features affect the rate and enantioselectivity of phase transfer catalyzed reactions. Described in this paper is the development of quantitative structure-activity relationships (QSAR) and -selectivity relationships (QSSR) for the alkylation of a protected glycine imine with libraries of quaternary ammonium ion catalysts. Catalyst descriptors including ammonium ion accessibility, interfacial adsorption affinity, and partition coefficient were found to correlate meaningfully with catalyst activity. The physical nature of the descriptors was rationalized through differing contributions of the interfacial and extraction mechanisms to the reaction under study. The variation in the observed enantioselectivity was rationalized employing a comparative molecular field analysis (CoMFA) using both the steric and electrostatic fields of the catalysts. A qualitative analysis of the developed model reveals preferred regions for catalyst binding to afford both configurations of the alkylated product.