Halogen bonding as a modern molecular
interaction has received
increasing attention not only in materials sciences but also in biological
systems and drug discovery. Thus, there is a growing demand for fast,
efficient, and easily applicable tailor-made tools supporting the
use of halogen bonds in molecular design and medicinal chemistry.
The potential strength of a halogen bond is dependent on several properties
of the σ-hole donor, e.g., a (hetero)aryl halide, and the σ-hole
acceptor, a nucleophile with n or π electron density. Besides
the influence of the interaction geometry and the type of acceptor,
significant tuning effects on the magnitude of the σ-hole can
be observed, caused by different (hetero)aromatic scaffolds and their
substitution patterns. The most positive electrostatic potential on
the isodensity surface (V
max), representing
the σ-hole, has been widely used as the standard descriptor
for the magnitude of the σ-hole and the strength of the halogen
bond. Calculation of V
max using quantum-mechanical
methods at a reasonable level of theory is time-consuming and thus
not applicable for larger numbers of compounds in drug discovery projects.
Herein we present a tool for the prediction of this descriptor based
on a machine-learned model with a speedup of 5 to 6 orders of magnitude
relative to MP2 quantum-mechanical calculations. According to the
test set, the squared correlation coefficient is greater than 0.94.