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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.

Vinylbenziodoxolones have recently been identified as efficient hypervalent iodine(III) reagents for electrophilic vinylations under transition metal-free conditions. Their unique reactivity allows synthesis of either internal or terminal alkenes, depending on the nucleophile class. This paper constitutes the first mechanistic investigation of VBX vinylations, and makes use of NMR studies, deuterium labelling and computations to rationalize the observed regio- and stereochemical outcome. Internal alkene formation in S-vinylation was found to proceed through the ligand coupling mechanism typical of diaryliodonium salts, whereas terminal alkene formation in P-vinylations took place via a phosphinous acid-coordinated VBX complex, which underwent concerted deprotonation and Michael-type addition. Subsequent base-assisted protonation and E2 elimination delivered the terminal alkene. The findings can be used to predict the regioselectivity in vinylations of other nucleophile classes.

Understanding the mechanisms of enzymatic catalysis requires a detailed understanding of the complex interplay of structure and dynamics of large systems that is a challenge for both experimental and computational approaches. More importantly, the computational demands of QM/MM simulations mean that the dynamics of the reaction can only be considered on a timescale of nanoseconds even though the conformational changes needed to reach the catalytically active state happen on a much slower timescale. Here we demonstrate an alternative approach that uses transition state force fields (TSFFs) derived by the quantum-guided molecular mechanics (Q2MM) method that provides a consistent treatment of the entire system at the classical molecular mechanics level and allows simulations at the microsecond timescale. Application of this approach to the second hydride transfer transition state of HMG-CoA reductase from Pseudomonas mevalonii (PmHMGR) identified three remote residues, R396, E399 and L407, (15-27 angstrom away from the active site) that have a remote dynamic effect on enzyme activity. The predictions were subsequently validated experimentally via site-directed mutagenesis. These results show that microsecond timescale MD simulations of transition states are possible and can predict rather than just rationalize remote allosteric residues.

Here we describe the development of biaryl 2,5-diphenylphospholanes as a new class of C-2-symmetric, monodentate ligands for asymmetric Suzuki-Miyaura (ASM) reactions. Screening of a series of exemplary phospholanes led to the identification of two ligands that were used to prepare a range of atropisomeric biaryl and heterobiaryl products with good to excellent levels of enantioselectivity (up to 97:3 e.r.) under mild conditions. DFT studies suggest that the formation of a constraining ligand pocket and coordination of one of the biaryl methoxy groups in the optimised ligands to the metal centre is crucial for restricting conformational freedom in the bond-forming step.

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., QC 20210323