We quantify electronic similarity across conformers of eight sulfur-containing systems using localization–delocalization matrices (LDMs) from Quantum Theory of Atoms in Molecule (QTAIM) and the Frobenius distance (FD) as a matrix-based metric. After conformer sampling and \(\:\omega\:\) B97X-D/6-311G(2d, p) optimization, we analyzed 630 unique conformers (allyl mercaptan, 2-propenesulfenic acid, diallyl monosulfide, diallyl disulfide, diallyl trisulfide, allicin, and the \(\:E/Z\) isomers of ajoene). Conformers were grouped according to their QTAIM molecular graph classification into acyclic, ring critical point (RCP), or cage critical point (CCP) types. Pairwise FDR values within and across topological classes are generally small (typically ≤ 0.10–0.15), indicating that electron density is largely preserved despite substantial conformational flexibility and distinct topological features, including ring and cage critical points. Analysis of bond critical points associated with these features reveals low electron density and negligible delocalization, consistent with weak intramolecular interactions that modify topology without inducing significant electronic redistribution. Boltzmann-weighted averaging at 298 K yields ensemble-averaged LDMs that closely resemble those of the lowest-energy conformers, and equivalent trends are obtained at physiological temperature (≈ 300 K), confirming the robustness of the electronic similarity under thermally accessible conditions. A gradual increase in mean FDR from R1 (≈ 0.07) to R4 (≈ 0.11) reflects modest electronic reorganization with increasing regime index rather than abrupt changes. Overall, these results demonstrate that pronounced topological differences do not necessarily imply significant electronic reorganization and establish LDM-based descriptors (FD, FDW, Nloc, and Ndeloc) as compact and robust measures of conformer similarity, clarifying when single-conformer approximations are adequate and providing a foundation for future extensions to protein-binding relevant conformational landscapes.
