The Effect of Off-Center $\sigma$-Hole on the Atom-Centered Partial Charges in Halogenated Molecules
Aneta Leskourov\'a, Michal H. Kol\'a\v{r}

TL;DR
This study investigates how off-center $\sigma$-holes, modeled as pseudo-atoms, influence atom-centered partial charges in halogenated molecules, revealing their significant impact within three bonds and improving charge fitting accuracy.
Contribution
It provides a comprehensive analysis of the effect of off-center $\sigma$-holes on partial charges in halogenated molecules, demonstrating the importance of pseudo-atoms for accurate electrostatic modeling.
Findings
Pseudo-atoms improve charge fitting in most molecules.
$\sigma$-holes influence atoms within three covalent bonds.
Including pseudo-atoms enhances electrostatic descriptions.
Abstract
Partial atomic charges belong to key concepts of computational chemistry. In some cases, however, they fail in describing the electrostatics of molecules. One such example is the -hole, a region of positive electrostatic potential located on halogens and other atoms. In molecular mechanics, the -hole is often modeled as a pseudo-atom with a positive partial charge located off the halogen nucleus. Here we address a question, to what extent the pseudo-atom affects partial charges of other atoms in the molecule. To this aim, we have thoroughly analyzed partial charges of over 2300 halogenated molecules from the ZINC database calculated by the Restricted Electrostatic Potential (RESP) method and compared them with the charges fitted by RESP including the pseudo-atom. We show that the pseudo-atom improves charge fitting for a vast majority of molecules. The -hole,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCrystallography and molecular interactions · Computational Drug Discovery Methods · Machine Learning in Materials Science
