TCFH-NMI Ketone Synthesis Inspired by Nucleophilicity Scales

N,N,N',N'-Tetramethylchloroformamidinium hexafluorophosphate (TCFH) and N-methylimidazole (NMI) enable the facile and practical reaction of carboxylic acids with amines, alcohols, and thiols to form amides, esters, and thioesters. To develop a mild synthesis of ketones with TCFH-NMI directly from carboxylic acids at room temperature, the Mayr nucleophilicity scale was used to compare the N values of competent nucleophiles to potential carbon-centered nucleophiles, identifying pyrroles and indoles as successful substrates when N ≥ 10.

Quantitative Structure-Reactivity Relationships for Synthesis Planning: The Benzhydrylium Case

Selective and feasible reactions are among the top targets in synthesis planning. Mayr's approach to quantifying chemical reactivity has greatly facilitated the planning process, but reactivity parameters for new compounds require time-consuming experiments. In the past decade, data-driven modeling has been gaining momentum in the field, as it shows promise in terms of efficient reactivity prediction. However, state-of-the-art models use quantum chemical data as input, which prevent access to real-time planning in organic synthesis. Here, we present a novel data-driven workflow for predicting reactivity parameters of molecules that takes only structural information as input, enabling de facto real-time reactivity predictions. We use the well-understood chemical space of benzhydrylium ions as an example to demonstrate the functionality of our approach and the performance of the resulting quantitative structure-reactivity relationships (QSRRs). Our results suggest that it is straightforward to build low-cost QSRR models that are accurate, interpretable, and transferable to unexplored systems within a given scope of application. Moreover, our QSRR approach suggests that Hammett σ parameters are only approximately additive.

One-Bond-Nucleophilicity and -Electrophilicity Parameters: An Efficient Ordering System for 1,3-Dipolar Cycloadditions

Diazoalkanes are ambiphilic 1,3-dipoles that undergo fast Huisgen cycloadditions with both electron-rich and electron-poor dipolarophiles but react slowly with alkenes of low polarity. Frontier molecular orbital (FMO) theory considering the 3-center-4-electron π-system of the propargyl fragment of diazoalkanes is commonly applied to rationalize these reactivity trends. However, we recently found that a change in the mechanism from cycloadditions to azo couplings takes place due to the existence of a previously overlooked lower-lying unoccupied molecular orbital. We now propose an alternative approach to analyze 1,3-dipolar cycloaddition reactions, which relies on the linear free energy relationship lg k₂(20 °C) = s_N(N + E) (eq 1) with two solvent-dependent parameters (N, s_N) to characterize nucleophiles and one parameter (E) for electrophiles. Rate constants for the cycloadditions of diazoalkanes with dipolarophiles were measured and compared with those calculated for the formation of zwitterions by eq 1. The difference between experimental and predicted Gibbs energies of activation is interpreted as the energy of concert, i.e., the stabilization of the transition states by the concerted formation of two new bonds. By linking the plot of lg k₂ vs N for nucleophilic dipolarophiles with that of lg k₂ vs E for electrophilic dipolarophiles, one obtains V-shaped plots which provide absolute rate constants for the stepwise reactions on the borderlines. These plots furthermore predict relative reactivities of dipolarophiles in concerted, highly asynchronous cycloadditions more precisely than the classical correlations of rate constants with FMO energies or ionization potentials. DFT calculations using the SMD solvent model confirm these interpretations.

The computational road to reactivity scales

Reactivity scales are useful research tools for chemists, both experimental and computational. However, to determine the reactivity of a single molecule, multiple measurements need to be carried out, which is a time-consuming and resource-intensive task. In this Tutorial Review, we present alternative approaches for the efficient generation of quantitative structure-reactivity relationships that are based on quantum chemistry, supervised learning, and uncertainty quantification. First published in 2002, we observe a tendency for these relationships to become not only more predictive but also more interpretable over time.

DFT-Guided Discovery of Ethynyl-Triazolyl-Phosphinates as Modular Electrophiles for Chemoselective Cysteine Bioconjugation and Profiling

We report the density functional theory (DFT) guided discovery of ethynyl-triazolyl-phosphinates (ETPs) as a new class of electrophilic warheads for cysteine selective bioconjugation. By using CuI -catalysed azide alkyne cycloaddition (CuAAC) in aqueous buffer, we were able to access a variety of functional electrophilic building blocks, including proteins, from diethynyl-phosphinate. ETP-reagents were used to obtain fluorescent peptide-conjugates for receptor labelling on live cells and a stable and a biologically active antibody-drug-conjugate. Moreover, we were able to incorporate ETP-electrophiles into an azide-containing ubiquitin under native conditions and demonstrate their potential in protein-protein conjugation. Finally, we showcase the excellent cysteine-selectivity of this new class of electrophile in mass spectrometry based, proteome-wide cysteine profiling, underscoring the applicability in homogeneous bioconjugation strategies to connect two complex biomolecules.

Nucleophilicity Prediction Using Graph Neural Networks

The quantitative description between chemical reaction rates and nucleophilicity parameters plays a crucial role in organic chemistry. In this regard, the formula proposed by Mayr et al. and the constructed reactivity database are important representatives. However, the determination of Mayr's nucleophilicity parameter N often requires time-consuming experiments with reference electrophiles in the solvent. Several machine learning (ML)-based models have been proposed to realize the data-driven prediction of N in recent years. However, in addition to DFT-calculated electronic descriptors, most of them also use a set of artificially predefined structural descriptors as input, which may result in a biased representation of the nucleophile's structural information depending on descriptors' definition preference. Compared with traditional ML algorithms, graph neural networks (GNNs) can naturally take the molecule's structural information into account by applying the message passing technique. We herein proposed a SchNet-based GNN model that only takes the molecular conformation and solvent type as input. The model achieves a comparable performance to the previous benchmark study on 10-fold cross-validation of 894 data points (R² = 0.91, RMSE = 2.25). To enhance the model's ability to capture the molecule's electronic information, some DFT-calculated parameters are then incorporated into the model via graph global features, and substantial improvement is achieved in the prediction precision (R² = 0.95, RMSE = 1.63). These results demonstrate that both structural and electronic information are important for the prediction of N, and GNN can integrate these two kinds of information more effectively.

Uncertainty Quantification of Reactivity Scales

According to Mayr, polar organic synthesis can be rationalized by a simple empirical relationship linking bimolecular rate constants to as few as three reactivity parameters. Here, we propose an extension to Mayr's reactivity method that is rooted in uncertainty quantification and transforms the reactivity parameters into probability distributions. Through uncertainty propagation, these distributions can be transformed into uncertainty estimates for bimolecular rate constants. Chemists can exploit these virtual error bars to enhance synthesis planning and to decrease the ambiguity of conclusions drawn from experimental data. We demonstrate the above at the example of the reference data set released by Mayr and co-workers [J. Am. Chem. Soc. 2001, 123, 9500; J. Am. Chem. Soc. 2012, 134, 13902]. As by-product of the new approach, we obtain revised reactivity parameters for 36 π-nucleophiles and 32 benzhydrylium ions.

Bond Energies of Enamines

Energetics of reactive intermediates underlies their reactivity. The availability of these data provides a rational basis for understanding and predicting a chemical reaction. We reported here a comprehensive computational study on the energetics of enamine intermediates that are fundamental in carbonyl chemistry. Accurate density functional theory (DFT) calculations were performed to determine the bond energies of enamines and their derived radical intermediates. These efforts led to the compilation of a database of enamine energetics including a thermodynamic index such as free-energy stability, bond dissociation energy (BDE), and acid dissociation constant (pK _a) as well as a kinetic index such as nucleophilicity and electrophilicity. These data were validated by relating to experimentally determined parameters and their relevance and utility were discussed in the context of modern enamine catalysis. It was found that pK _a values of enamine radical cations correlated well with redox potentials of their parent enamines, the former could be used to rationalize the proton-transfer behavior of enamine radical cations. An analysis of the BDE of enamine radical cations indicated that these species underwent facile β-C-H hydrogen transfer, in line with the known oxidative enamine catalysis. The enamine energetics offers the possibility of a systematic evaluation of the reactivities of enamines and related radicals, which would provide useful guidance in exploring new enamine transformations.

A machine learning approach for predicting the nucleophilicity of organic molecules

Nucleophilicity provides important information about the chemical reactivity of organic molecules. Experimental determination of the nucleophilicity parameter is a tedious and resource-intensive approach. Herein, we present a novel machine learning protocol that uses key structural descriptors to predict the nucleophilicities of organic molecules, which agree well with the experimental values. A data driven approach was used where quantum mechanical molecular and thermodynamic descriptors from a wide range of structurally diverse nucleophiles and relevant solvents were extracted and modelled using advanced algorithms against the experimentally available nucleophilicity values. Despite the structural diversity of nucleophiles, we are able to achieve statistically robust models with a high predictive power using tree-based and neural network algorithms trained on an in-house developed unique dataset consisting of 752 nucleophilicity values and 27 molecular descriptors.

Predicting Solvent-Dependent Nucleophilicity Parameter with a Causal Structure Property Relationship

Solvent-dependent reactivity is a key aspect of synthetic science, which controls reaction selectivity. The contemporary focus on new, sustainable solvents highlights a need for reactivity predictions in different solvents. Herein, we report the excellent machine learning prediction of the nucleophilicity parameter N in the four most-common solvents for nucleophiles in the Mayr's reactivity parameter database (R² = 0.93 and 81.6% of predictions within ±2.0 of the experimental values with Extra Trees algorithm). A Causal Structure Property Relationship (CSPR) approach was utilized, with focus on the physicochemical relationships between the descriptors and the predicted parameters, and on rational improvements of the prediction models. The nucleophiles were represented with a series of electronic and steric descriptors and the solvents were represented with principal component analysis (PCA) descriptors based on the ACS Solvent Tool. The models indicated that steric factors do not contribute significantly, because of bias in the experimental database. The most important descriptors are solvent-dependent HOMO energy and Hirshfeld charge of the nucleophilic atom. Replacing DFT descriptors with Parameterization Method 6 (PM6) descriptors for the nucleophiles led to an 8.7-fold decrease in computational time, and an ∼10% decrease in the percentage of predictions within ±2.0 and ±1.0 of the experimental values.

Methyl Cation Affinities of Canonical Organic Functional Groups

Methyl cation affinities are calculated for the canonical nucleophilic functional groups in organic chemistry. These methyl cation affinities, calculated with a solvation model (MCA*), give an emprical correlation with the Ns_N term from the Mayr equation under aprotic conditions when they are scaled to the Mayr reference cation (4-MeOC₆H₄)₂CH⁺ (Mayr E = 0). Highly reactive anionic nucleophiles were found to give a separate correlation, while some ylides and phosphorus compounds were determined to give a poor correlation. MCA*s are estimated for a broad range of simple molecules representing the canonical functional groups in organic chemistry. On the basis of a linear correlation, we estimate the range of nucleophilicities of organic functional groups, ranging from a C-C bond to a hypothetical tert-butyl carbanion, toward the reference electrophile to be about 50 orders of magnitude.

Nucleophilicity Prediction via Multivariate Linear Regression Analysis

The concept of nucleophilicity is at the basis of most transformations in chemistry. Understanding and predicting the relative reactivity of different nucleophiles is therefore of paramount importance. Mayr’s nucleophilicity scale likely represents the most complete collection of reactivity data, which currently includes over 1200 nucleophiles. Several attempts have been made to theoretically predict Mayr’s nucleophilicity parameters N based on calculation of molecular properties, but a general model accounting for different classes of nucleophiles could not be obtained so far. We herein show that multivariate linear regression analysis is a suitable tool for obtaining a simple model predicting N for virtually any class of nucleophiles in different solvents for a set of 341 data points. The key descriptors of the model were found to account for the proton affinity, solvation energies, and sterics.

Kinetics and Mechanism of Oxirane Formation by Darzens Condensation of Ketones: Quantification of the Electrophilicities of Ketones

The kinetics of epoxide formation by Darzens condensation of aliphatic ketones 1 with arylsulfonyl-substituted chloromethyl anions 2 (ArSO₂CHCl^-) have been determined photometrically in DMSO solution at 20 °C. The reactions proceed via nucleophilic attack of the carbanions at the carbonyl group to give intermediate halohydrin anions 4, which subsequently cyclize with formation of the oxiranes 3. Protonation of the reaction mixture obtained in THF solution at low temperature allowed the intermediates to be trapped and the corresponding halohydrins 4-H to be isolated. Crossover experiments, i.e., deprotonation of the halohydrins 4-H in the presence of a trapping reagent for the regenerated arylsulfonyl-substituted chloromethyl anions 2, provided the relative rates of backward ( k_-CC) and ring closure ( k_rc) reactions of the intermediates. Combination of the kinetic data ( k₂^exptl) with the splitting ratio ( k_-CC/ k_rc) gave the second-order rate constants k_CC for the attack of the carbanions 2 at the ketones 1. These k_CC values and the previously reported reactivity parameters N and s_N for the arylsulfonyl-substituted chloromethyl anions 2 allowed us to use the linear free energy relationship log k₂(20 °C) = s_N( N + E) for deriving the electrophilicity parameters E of the ketones 1 and thus predict potential nucleophilic reaction partners. Density functional theory calculations of the intrinsic reaction pathways showed that the reactions of the ketones 1 with the chloromethyl anions 2 yield two rotational isomers of the intermediate halohydrin anions 4, only one of which can cyclize while the other undergoes retroaddition because the barrier for rotation is higher than that for reversal to the reactants 1 and 2. The electrophilicity parameters E correlate moderately with the lowest unoccupied molecular orbital energies of the carbonyl groups, very poorly with Parr's electrophilicity indices, and best with the methyl anion affinities calculated for DMSO solution.

Quantification and Theoretical Analysis of the Electrophilicities of Michael Acceptors

In order to quantify the electrophilic reactivities of common Michael acceptors, we measured the kinetics of the reactions of monoacceptor-substituted ethylenes (H₂C═CH-Acc, 1) and styrenes (PhCH═CH-Acc, 2) with pyridinium ylides 3, sulfonium ylide 4, and sulfonyl-substituted chloromethyl anion 5. Substitution of the 57 measured second-order rate constants (log k) and the previously reported nucleophile-specific parameters N and s_N for 3-5 into the correlation log k = s_N(E + N) allowed us to calculate 15 new empirical electrophilicity parameters E for Michael acceptors 1 and 2. The use of the same parameters s_N, N, and E for these different types of reactions shows that all reactions proceed via a common rate-determining step, the nucleophilic attack of 3-5 at the Michael acceptors with formation of acyclic intermediates, which subsequently cyclize to give tetrahydroindolizines (stepwise 1,3-dipolar cycloadditions with 3) and cyclopropanes (with 4 and 5), respectively. The electrophilicity parameters E thus determined can be used to calculate the rates of the reactions of Michael acceptors 1 and 2 with any nucleophile of known N and s_N. DFT calculations were performed to confirm the suggested reaction mechanisms and to elucidate the origin of the electrophilic reactivities. While electrophilicities E correlate poorly with the LUMO energies and with Parr's electrophilicity index ω, good correlations were found between the experimentally observed electrophilic reactivities of 44 Michael acceptors and their calculated methyl anion affinities, particularly when solvation by dimethyl sulfoxide was taken into account by applying the SMD continuum solvation model. Because of the large structural variety of Michael acceptors considered for these correlations, which cover a reactivity range of 17 orders of magnitude, we consider the calculation of methyl anion affinities to be the method of choice for a rapid estimate of electrophilic reactivities.

Computational organic chemistry: bridging theory and experiment in establishing the mechanisms of chemical reactions

Understanding the mechanisms of chemical reactions, especially catalysis, has been an important and active area of computational organic chemistry, and close collaborations between experimentalists and theorists represent a growing trend. This Perspective provides examples of such productive collaborations. The understanding of various reaction mechanisms and the insight gained from these studies are emphasized. The applications of various experimental techniques in elucidation of reaction details as well as the development of various computational techniques to meet the demand of emerging synthetic methods, e.g., C-H activation, organocatalysis, and single electron transfer, are presented along with some conventional developments of mechanistic aspects. Examples of applications are selected to demonstrate the advantages and limitations of these techniques. Some challenges in the mechanistic studies and predictions of reactions are also analyzed.

Scales of Lewis basicities toward C-centered Lewis acids (carbocations)

Equilibria for the reactions of benzhydryl cations (Ar2CH(+)) with phosphines, tert-amines, pyridines, and related Lewis bases were determined photometrically in CH2Cl2 and CH3CN solution at 20 °C. The measured equilibrium constants can be expressed by the sum of two parameters, defined as the Lewis Acidity (LA) of the benzhydrylium ions and the Lewis basicity (LB) of the phosphines, pyridines, etc. Least-squares minimization of log K = LA + LB with the definition LA = 0 for (4-MeOC6H4)2CH(+) gave a Lewis acidity scale for 18 benzhydrylium ions covering 18 orders of magnitude in CH2Cl2 as well as Lewis basicities (with respect to C-centered Lewis acids) for 56 bases. The Lewis acidities correlated linearly with the quantum chemically calculated (B3LYP/6-311++G(3df,2pd)//B3LYP/6-31G(d,p) level) methyl anion affinities of the corresponding benzhydrylium ions, which can be used as reference compounds for characterizing a wide variety of Lewis bases. The equilibrium measurements were complemented by isothermal titration calorimetry studies. Rates of SN1 solvolyses of benzhydryl chlorides, bromides, and tosylates derived from E(13-33)(+), i.e., from highly reactive carbocations, correlate excellently with the corresponding Lewis acidities and the quantum chemically calculated methyl anion affinities. This correlation does not hold for solvolyses of derivatives of the better stabilized amino-substituted benzhydrylium ions E(1-12)(+). In contrast, the correlation between electrophilic reactivities and Lewis acidities (or methyl anion affinities) is linear for all donor-substituted benzhydrylium ions E(1-21)(+), while the acceptor-substituted benzhydrylium ions E(26-33)(+) react more slowly than expected from their thermodynamic stabilities. The boundaries of linear rate-equilibrium relationships were thus defined.

NHC-catalyzed homoenolate reaction of enals and nitroalkenes: computational study of mechanism, chemoselectivity and stereoselectivity

A mechanistic study on the NHC-catalyzed homoenolate reaction of enals and nitroalkenes has been performed with the aid of DFT calculations.