A Quantitative Model for Alkane Nucleophilicity Based on C-H Bond Structural/Topological Descriptors

Mechanism and Selectivity of Copper-Catalyzed Bromination of Distal C(sp3)-H Bonds

Unactivated C(sp3)-H bonds are the most challenging substrate class for transition metal-catalyzed C-H halogenation. Recently, the Yu group [Liu, T.; Myers, M. C.; Yu, J. Q. Angew. Chem., Int. Ed.2017, 56 (1), 306-309] has demonstrated that a CuII/phenanthroline catalyst and BrN3, generated in situ from NBS and TMSN3 precursors, can achieve selective C-H bromination distal to a directing group. The current understanding of the mechanism of this reaction has left numerous questions unanswered. Here, we investigated the mechanism of Cu-catalyzed C(sp3)-H bromination with distal site selectivity using density functional theory calculations. We found that this reaction starts with the Br-atom transfer from BrN3 to the Cu center that occurs via a small energy barrier at the singlet-triplet state seam of crossing. In the course of this reaction, the presence of the N-H bond in the substrate is critically important and acts as a directing group for enhancing the stability of the catalyst-substrate interaction and for the recruitment of the substrate to the catalyst. The required C-centered radical substrate formation occurs via direct C-H dehydrogenation by the Cu-coordinated N3 radical, rather than via the previously proposed N-H bond dehydrogenation and then the 1,5-H transfer from the γ-(C-H) bond to the N-radical center pathway. The C-H bond activation by the azide radical is a regioselectivity-controlling step. The following bromination of the C-centered radical by the Cu-coordinated bromine completes the product formation. This reaction step is the rate-limiting step, occurs at the singlet-to-triplet state seam of the crossing point, and is exergonic.

Enantioselective Catalysis with Pyrrolidinyl Gold(I) Complexes: DFT and NEST Analysis of the Chiral Binding Pocket

A new generation of chiral gold(I) catalysts based on variations of complexes with JohnPhos-type ligands with a remote C₂-symmetric 2,5-diarylpyrrolidine have been synthesized with different substitutions at the top and bottom aryl rings: from replacing the phosphine by a N-heterocyclic carbene (NHC) to increasing the steric hindrance with bis- or tris-biphenylphosphine scaffolds, or by directly attaching the C₂-chiral pyrrolidine in the ortho-position of the dialkylphenyl phosphine. The new chiral gold(I) catalysts have been tested in the intramolecular [4+2] cycloaddition of arylalkynes with alkenes and in the atroposelective synthesis of 2-arylindoles. Interestingly, simpler catalysts with the C₂-chiral pyrrolidine in the ortho-position of the dialkylphenyl phosphine led to the formation of opposite enantiomers. The chiral binding pockets of the new catalysts have been analyzed by DFT calculations. As revealed by non-covalent interaction plots, attractive non-covalent interactions between substrates and catalysts direct specific enantioselective folding. Furthermore, we have introduced the open-source tool NEST, specifically designed to account for steric effects in cylindrical-shaped complexes, which allows predicting experimental enantioselectivities in our systems.

Leveraging Regio- and Stereoselective C(sp3)-H Functionalization of Silyl Ethers to Train a Logistic Regression Classification Model for Predicting Site-Selectivity Bias

The C-H functionalization of silyl ethers via carbene-induced C-H insertion represents an efficient synthetic disconnection strategy. In this work, site- and stereoselective C(sp3)-H functionalization at α, γ, δ, and even more distal positions to the siloxy group has been achieved using donor/acceptor carbene intermediates. By exploiting the predilections of Rh2(R-TCPTAD)4 and Rh2(S-2-Cl-5-BrTPCP)4 catalysts to target either more electronically activated or more spatially accessible C-H sites, respectively, divergent desired products can be formed with good diastereocontrol and enantiocontrol. Notably, the reaction can also be extended to enable desymmetrization of meso silyl ethers. Leveraging the broad substrate scope examined in this study, we have trained a machine learning classification model using logistic regression to predict the major C-H functionalization site based on intrinsic substrate reactivity and catalyst propensity for overriding it. This model enables prediction of the major product when applying these C-H functionalization methods to a new substrate of interest. Applying this model broadly, we have demonstrated its utility for guiding late-stage functionalization in complex settings and developed an intuitive visualization tool to assist synthetic chemists in such endeavors.

Direct gem-Difluoroalkenylation of X-H Bonds with Trifluoromethyl Ketone N-Triftosylhydrazones for Synthesis of Tetrasubstituted Heteroatomic gem-Difluoroalkenes

The direct gem-difluoroalkenylation of X-H bonds represents the most straightforward approach to access heteroatomic gem-difluoroalkenes that, as the isostere of the carbonyl group, have great potency in drug discovery. However, the construction of tetrasubstituted heteroatomic gem-difluoroalkenes by this strategy is still an unsolved problem. Here, we report the first direct X-H bond gem-difluoroalkenylation of amines and alcohols with trifluoromethyl ketone N-triftosylhydrazones under silver (for (hetero)aryl hydrazones) or rhodium (for alkyl hydrazones), thereby providing a most powerful method for the synthesis of tetrasubstituted heteroatomic gem-difluoroalkenes. This method features a broad substrate scope, high product yield, excellent functional group tolerance, and operational simplicity (open air conditions). Moreover, the site-specific replacement of the carbonyl group with a gem-difluorovinyl ether bioisostere in drug Trimebutine and the post-modification of bioactive molecules demonstrates potential use in medicinal research. Finally, the reaction mechanism was investigated by combining experiments and DFT calculations, and disclosed that the key step of HF elimination occurred via five-membered ring transition state, and the difference in the electrophilicity of Ag- and Rh-carbenes as well as the multiple intermolecular interactions rendered the effectiveness of Rh catalyst selectively for alkyl hydrazones.

Computational Study of Key Mechanistic Details for a Proposed Copper (I)-Mediated Deconstructive Fluorination of N-Protected Cyclic Amines

Using calculations, we show that a proposed Cu(I)-mediated deconstructive fluorination of N-benzoylated cyclic amines with Selectfluor^® is feasible and may proceed through: (a) substrate coordination to a Cu(I) salt, (b) iminium ion formation followed by conversion to a hemiaminal, and (c) fluorination involving C-C cleavage of the hemiaminal. The iminium ion formation is calculated to proceed via a F-atom coupled electron transfer (FCET) mechanism to form, formally, a product arising from oxidative addition coupled with electron transfer (OA + ET). The subsequent β-C-C cleavage/fluorination of the hemiaminal intermediate may proceed via either ring-opening or deformylative fluorination pathways. The latter pathway is initiated by opening of the hemiaminal to give an aldehyde, followed by formyl H-atom abstraction by a TEDA²⁺ radical dication, decarbonylation, and fluorination of the C3-radical center by another equivalent of Selectfluor^®. In general, the mechanism for the proposed Cu(I)- mediated deconstructive C-H fluorination of N-benzoylated cyclic amines (LH) by Selectfluor^® was calculated to proceed analogously to our previously reported Ag(I)-mediated reaction. In comparison to the Ag(I)-mediated process, in the Cu(I)-mediated reaction the iminium ion formation and hemiaminal fluorination have lower associated energy barriers, whereas the product release and catalyst re-generation steps have higher barriers.

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.

Copper-Catalyzed Dehydrogenative Amidation of Light Alkanes

The functionalization of C-H bonds in light alkanes, particularly to form C-N bonds, remains a challenge. We report the dehydrogenative coupling of amides with C1-C4 hydrocarbons to form N-alkyl amide products with tBuOOtBu as oxidant, and a copper complex of a phenanthroline-type ligand as catalyst. The reactions occurred in good yields in benzene or supercritical carbon dioxide as solvents. This strategy allowed for the determination of the relative reactivity of these alkane C-H bonds toward this amination process and showed, in contrast to prior work with larger alkanes, that the reactivity correlated with bond dissociation energies.

John PC, Kim S, Paton RS. Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. In this Account, we highlight the application and suitability of different representations, from expert-guided "engineered" descriptors to automatically "learned" features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data.The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used.Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as "hand-crafted" molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure-property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors.Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule's weakest bond are used in simple physical models of site-selectivity and reactivity.

tmQM Dataset-Quantum Geometries and Properties of 86k Transition Metal Complexes

We report the transition metal quantum mechanics (tmQM) data set, which contains the geometries and properties of a large transition metal-organic compound space. tmQM comprises 86,665 mononuclear complexes extracted from the Cambridge Structural Database, including Werner, bioinorganic, and organometallic complexes based on a large variety of organic ligands and 30 transition metals (the 3d, 4d, and 5d from groups 3 to 12). All complexes are closed-shell, with a formal charge in the range {+1, 0, -1}e. The tmQM data set provides the Cartesian coordinates of all metal complexes optimized at the GFN2-xTB level, and their molecular size, stoichiometry, and metal node degree. The quantum properties were computed at the DFT(TPSSh-D3BJ/def2-SVP) level and include the electronic and dispersion energies, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, HOMO/LUMO gap, dipole moment, and natural charge of the metal center; GFN2-xTB polarizabilities are also provided. Pairwise representations showed the low correlation between these properties, providing nearly continuous maps with unusual regions of the chemical space, for example, complexes combining large polarizabilities with wide HOMO/LUMO gaps and complexes combining low-energy HOMO orbitals with electron-rich metal centers. The tmQM data set can be exploited in the data-driven discovery of new metal complexes, including predictive models based on machine learning. These models may have a strong impact on the fields in which transition metal chemistry plays a key role, for example, catalysis, organic synthesis, and materials science. tmQM is an open data set that can be downloaded free of charge from https://github.com/bbskjelstad/tmqm.

Gold-Catalyzed Synthesis of Small Rings

Three- and four-membered rings, widespread motifs in nature and medicinal chemistry, have fascinated chemists ever since their discovery. However, due to energetic considerations, small rings are often difficult to assemble. In this regard, homogeneous gold catalysis has emerged as a powerful tool to construct these highly strained carbocycles. This review aims to provide a comprehensive summary of all the major advances and discoveries made in the gold-catalyzed synthesis of cyclopropanes, cyclopropenes, cyclobutanes, cyclobutenes, and their corresponding heterocyclic or heterosubstituted analogs.