Computational tools for the prediction of site- and regioselectivity of organic reactions

The regio- and site-selectivity of organic reactions is one of the most important aspects when it comes to synthesis planning. Due to that, massive research efforts were invested into computational models for regio- and site-selectivity prediction, and the introduction of machine learning to the chemical sciences within the past decade has added a whole new dimension to these endeavors. This review article walks through the currently available predictive tools for regio- and site-selectivity with a particular focus on machine learning models while being organized along the individual reaction classes of organic chemistry. Respective featurization techniques and model architectures are described and compared to each other; applications of the tools to critical real-world examples are highlighted. This paper aims to serve as an overview of the field's status quo for both the intended users of the tools, that is synthetic chemists, as well as for developers to find potential new research avenues.

Origins of Regioselectivity in CuH-Catalyzed Hydrofunctionalization of Alkenes

Factors controlling the regioselectivity in alkene hydrocupration were computationally investigated using energy decomposition analysis. The results demonstrate that the Markovnikov-selective hydrocupration with electronically activated mono-substituted olefins is mostly affected by the destabilizing Pauli repulsion, which is due to the electron delocalization effect. The anti-Markovnikov-selective hydrocupration with 1,1-dialkyl-substituted terminal olefins is dominated by the repulsive electrostatic interactions, which is because of the unequal π electron distribution caused by the induction effect of alkyl substituents.

How the electron-deficient Cp ligand facilitates Rh-catalyzed annulations with alkynes

The dominant factors for the CpX ligand effects (Cp* versus CpE) on the reactivity for alkyne insertion into cationic and neutral rhodacycles are identified based on energy decomposition analysis.

Computational insights into strain-increase allylborations for alkylidenecyclopropanes

The origins of reactivity of strain-increase allylborations were computationally investigated. The low reactivity of vinylcyclopropyl boronates is due to weak electronic interactions between benzaldehyde and allylboronates. By increasing the acidity...

Computational study of Cu-catalyzed 1,2-hydrocarboxylation of 1,3-dienes with CO2: Pauli repulsion-controlled regioselectivity of Cu–Bpin additions

The mechanism and origin of regioselectivity of Cu-catalyzed 1,2-hydrocarboxylation of 1,3-dienes with CO2 were computationally investigated. The results show that CO2 not only acts as a carboxylation reagent, but also...

Origins of regio- and stereoselectivity in Cu-catalyzed alkyne difunctionalization with CO2 and organoboranes

The anti-to-Cu 1,2-migration of alkynyl boronates is critical for the 1,1-E-selective difunctionalization of terminal alkynes with CO2 and organoboranes.

Mechanism-Based Approach to Reagent Selection for Oxidative Carbon-Hydrogen Bond Cleavage Reactions

Numerous hydride-abstracting agents generate the same cationic intermediate, but substrate features such as intermediate cation stability, oxidation potential, and steric environment can influence reaction rates in an oxidant-dependent manner. This manuscript provides experimental data to illustrate the role that structural features play in the kinetics of hydride abstraction reactions with commonly used quinone-, oxoammonium ion-, and carbocation- based oxidants. Computational studies of the transition state structures and energies explain these results and energy decomposition analysis calculations reveal unique sensitivities to electrostatic attraction and steric repulsions. Rigorous rate studies of select reactions validated the capacity of the calculations to predict reactivity trends. Additionally, kinetics studies demonstrate the potential for product inhibition in DDQ-mediated reactions. These studies provide a clear guide to select the optimal oxidant for structurally disparate substrates and lead to predictions of reactivity that were validated experimentally.