Utilizing the σ-complex stability for quantifying reactivity in nucleophilic substitution of aromatic fluorides

A broadly applicable quantitative relative reactivity model for nucleophilic aromatic substitution (SNAr) using simple descriptors

We report a multivariate linear regression model able to make accurate predictions for the relative rate and regioselectivity of nucleophilic aromatic substitution (S_NAr) reactions based on the electrophile structure. This model uses a diverse training/test set from experimentally-determined relative S_NAr rates between benzyl alcohol and 74 unique electrophiles, including heterocycles with multiple substitution patterns. There is a robust linear relationship between the experimental S_NAr free energies of activation and three molecular descriptors that can be obtained computationally: the electron affinity (EA) of the electrophile; the average molecular electrostatic potential (ESP) at the carbon undergoing substitution; and the sum of average ESP values for the ortho and para atoms relative to the reactive center. Despite using only simple descriptors calculated from ground state wavefunctions, this model demonstrates excellent correlation with previously measured S_NAr reaction rates, and is able to accurately predict site selectivity for multihalogenated substrates: 91% prediction accuracy across 82 individual examples. The excellent agreement between predicted and experimental outcomes makes this easy-to-implement reactivity model a potentially powerful tool for synthetic planning.

Regioselective Transfer Hydrogenative Defluorination of Polyfluoroarenes Catalyzed by Bifunctional Azairidacycle

The catalytic hydrodefluorination (HDF) with a bifunctional azairidacycle using HCOOK was examined for cyano- and chloro-substituted fluoroarenes, including penta- and tetrafluorobenzonitriles, tetrafluoroterephthalonitrile, tetrafluorophthalonitrile, 3-chloro-2,4,5,6-tetrafluoropyridine, and 4-cyano-2,3,5,6-tetrafluoropyridine. The reaction was performed in the presence of a controlled amount of HCOOK with a substrate/catalyst ratio (S/C) of 100 in a 1:1 mixture of 1,2-dimethoxyethane (DME) and H2O at an ambient temperature of 30 °C to obtain partially fluorinated compounds with satisfactory regioselectivities. The C–F bond cleavage proceeded favorably at the para position of substituents other than fluorine, which is in consonance with the nucleophilic aromatic substitution mechanism. In the HDF of tetrafluoroterephthalonitrile and 4-cyano-2,3,5,6-tetrafluoropyridine, which do not contain a fluorine atom at the para position of the cyano group, the double defluorination occurred solely at the 2- and 5-positions, as confirmed by X-ray crystallography. The HDF of 3-chloro-2,4,5,6-tetrafluoropyridine gave preference to the C–F bond cleavage over the C–Cl bond cleavage, unlike the dehalogenation pathway via electron-transfer radical anion fragmentation. In addition, new azairidacycles with an electron-donating methoxy substituent on the C–N chelating ligand were synthesized and served as a catalyst precursor (0.2 mol%) for the transfer hydrogenative defluorination of pentafluoropyridine, leading to 2,3,5,6-tetrafluoropyridine with up to a turnover number (TON) of 418.

Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

Accurate prediction of chemical reactions in solution is challenging for current state-of-the-art approaches based on transition state modelling with density functional theory. Models based on machine learning have emerged as a promising alternative to address these problems, but these models currently lack the precision to give crucial information on the magnitude of barrier heights, influence of solvents and catalysts and extent of regio- and chemoselectivity. Here, we construct hybrid models which combine the traditional transition state modelling and machine learning to accurately predict reaction barriers. We train a Gaussian Process Regression model to reproduce high-quality experimental kinetic data for the nucleophilic aromatic substitution reaction and use it to predict barriers with a mean absolute error of 0.77 kcal mol⁻¹ for an external test set. The model was further validated on regio- and chemoselectivity prediction on patent reaction data and achieved a competitive top-1 accuracy of 86%, despite not being trained explicitly for this task. Importantly, the model gives error bars for its predictions that can be used for risk assessment by the end user. Hybrid models emerge as the preferred alternative for accurate reaction prediction in the very common low-data situation where only 100–150 rate constants are available for a reaction class. With recent advances in deep learning for quickly predicting barriers and transition state geometries from density functional theory, we envision that hybrid models will soon become a standard alternative to complement current machine learning approaches based on ground-state physical organic descriptors or structural information such as molecular graphs or fingerprints.

Hybrid reactivity models, combining mechanistic calculations and machine learning with descriptors, are used to predict barriers for nucleophilic aromatic substitution.

Design of Novel 4-Aminobenzofuroxans and Evaluation of Their Antimicrobial and Anticancer Activity

A series of novel 4-aminobenzofuroxan derivatives containing aromatic/aliphatic amines fragments was achieved via aromatic nucleophilic substitution reaction of 4,6-dichloro-5-nitrobenzofuroxan. The quantum chemistry calculations were performed to identify the factors affecting the regioselectivity of the reaction. The formation of 4-substituted isomer is favored both by its greater stability and the lower activation barrier. Antimicrobial activity of the obtained compounds has been evaluated and some of them were found to suppress effectively bacterial biofilm growth. Fungistatic activity of 4-aminobenzofuroxans were tested on two genetically distinct isolates of M. nivale. The effect of some benzofuroxan derivatives is likely to be more universal against different varieties of M. nivale compared with benzimidazole and carbendazim. Additionally, their anti-cancer activity in vitro has been tested. 4-aminofuroxans possessing aniline moiety showed a high selectivity towards MCF-7 and M-HeLa tumor cell lines. Moreover, they exhibit a significantly lower toxicity towards normal liver cells compared to Doxorubicin and Tamoxifen. Thus, benzofuroxans containing aromatic amines fragments in their structure are promising candidates for further development both as anti-cancer and anti-microbial agents.

Concerted Nucleophilic Aromatic Substitution Reactions

Recent developments in experimental and computational chemistry have identified a rapidly growing class of nucleophilic aromatic substitutions that proceed by concerted (cSN Ar) rather than classical, two-step, SN Ar mechanisms. Whereas traditional SN Ar reactions require substantial activation of the aromatic ring by electron-withdrawing substituents, such activating groups are not mandatory in the concerted pathways.

Concerted nucleophilic aromatic substitutions

Nucleophilic aromatic substitution (SNAr) is one of the most widely applied reaction classes in pharmaceutical and chemical research, providing a broadly useful platform for the modification of aromatic ring scaffolds. The generally accepted mechanism for SNAr reactions involves a two-step addition-elimination sequence via a discrete, non-aromatic Meisenheimer complex. Here we use 12C/13C kinetic isotope effect (KIE) studies and computational analyses to provide evidence that prototypical SNAr reactions in fact proceed through concerted mechanisms. The KIE measurements were made possible by a new technique that leverages the high sensitivity of 19F as an NMR nucleus to quantitate the degree of isotopic fractionation. This sensitive technique permits the measurement of KIEs on 10 mg of natural abundance material in one overnight acquisition. As a result, it provides a practical tool for performing detailed mechanistic analyses of reactions that form or break C-F bonds.

Mechanism and regioselectivity of electrophilic aromatic nitration in solution: the validity of the transition state approach

The potential energy surfaces in gas phase and in aqueous solution for the nitration of benzene, chlorobenzene, and phenol have been elucidated with density functional theory at the M06-2X/6-311G(d,p) level combined with the polarizable continuum solvent model (PCM). Three reaction intermediates have been identified along both surfaces: the unoriented π-complex (I), the oriented reaction complex (II), and the σ-complex (III). In order to obtain quantitatively reliable results for positional selectivity and for modeling the expulsion of the proton, it is crucial to take solvent effects into consideration. The results are in agreement with Olah’s conclusion from over 40 years ago that the transition state leading to (II) is the rate-determining step in activated cases, while it is the one leading to (III) for deactivated cases. The simplified reactivity approach of using the free energy for the formation of (III) as a model of the rate-determining transition state has previously been shown to be very successful for halogenations, but problematic for nitrations. These observations are rationalized with the geometric and energetic resemblance, and lack of resemblance respectively, between (III) and the corresponding rate determining transition state. At this level of theory, neither the σ-complex (III) nor the reaction complex (II) can be used to accurately model the rate-determining transition state for nitrations.

Expanding the palette of phenanthridinium cations

5,6-Disubstituted phenanthridinium cations have a range of redox, fluorescence and biological properties. Some properties rely on phenanthridiniums intercalating into DNA, but the use of these cations as exomarkers for the reactive oxygen species (ROS), superoxide, and as inhibitors of acetylcholine esterase (AChE) do not require intercalation. A versatile modular synthesis of 5,6-disubstituted phenanthridiniums that introduces diversity by Suzuki–Miyaura coupling, imine formation and microwave-assisted cyclisation is presented. Computational modelling at the density functional theory (DFT) level reveals that the novel displacement of the aryl halide by an acyclic N-alkylimine proceeds by an S(N)Ar mechanism rather than electrocyclisation. It is found that the displacement of halide is concerted and there is no stable Meisenheimer intermediate, provided the calculations consistently use a polarisable solvent model and a diffuse basis set.