Computational prediction of C–H hydricities and their use in predicting the regioselectivity of electron-rich C–H functionalisation reactions

• Rasmus M. Borup,
• Nicolai Ree and
• Jan H. Jensen

Beilstein J. Org. Chem. 2026, 22, 603–610, doi:10.3762/bjoc.22.46

• Rasmus M. Borup Nicolai Ree Jan H. Jensen Department of Chemistry, University of Copenhagen, Copenhagen, DK-2100, Denmark 10.3762/bjoc.22.46 Abstract We present HAlator, a fully automated quantum chemistry (QM) workflow for computing C–H hydricities and explore its potential in predicting the

• host of other reactivity predictors. Keywords: bond dissociation energy; hydricity; hydride affinity; hydride-transfer reactions; machine learning (ML); quantum chemistry (QM); Introduction Bond dissociation energies (BDEs) and pKa values for C–H bonds are often used to rationalise and predict the

• paper we present a quantum chemistry (QM)-based workflow for the automatic prediction of hydricities. We use this QM workflow to create a training set for an ML-based hydricity predictor and give a few examples of how the method can be used to rationalise the regioselectivity of a diverse set of

pKalculator: A pK_a predictor for C–H bonds

• Rasmus M. Borup,
• Nicolai Ree and
• Jan H. Jensen

Beilstein J. Org. Chem. 2024, 20, 1614–1622, doi:10.3762/bjoc.20.144

• . As molecular complexity increases, this task becomes more challenging. This paper introduces pKalculator, a quantum chemistry (QM)-based workflow for automatic computations of C–H pKa values, which is used to generate a training dataset for a machine learning (ML) model. The QM workflow is

• quantum chemistry (QM)-based workflow for the automatic computation of C–H pKa values in DMSO. The computed C–H pKa values are then used to generate training data for an ML model using LightGBM [6]. The QM-based workflow and the ML model are freely available under the MIT license. Methods Datasets We