How the affinity and targeted cavities were confirmed? As in our most recent work 9, 13 , the AutoDockVina (ADV) and OpenBabel using mmff94s force-field algorithms (PyRx-0.98/1.0 packages) were employed because of their high accuracies and world-wide ongoing improvements . ADV was employed to: i) selection of antihistamine drugs and their H1R targeted cavities, ii) quantify ADV-conformer affinities in ~ nM, iii) identify nearby amino acids in ADV docked complexes and iv) confirm the HR cavities targeted by ADV-conformers. A wide grid of 90x90x90 Å centered to the PyMol / centerofmass, surrounding most of the H1R model molecules was employed for blind-docking, while an smaller grid of 30x30x30 Å centered at coordinates 148x135x188 Å, surrounding the histamine binding sites were used for ADV docking to study cross-reactivities with H2R, H3R, H4R and Kv1.5. The output ADV-conformer docking-scores in -Kcal/mol were converted to ~ nM affinities by applying the formula, 10 9 *(exp (Kcal/mol/0.592) ). To identify the H1R amino acids nearby 4 Å distance of each ADV-conformer, a Python script was run in PyMol-opened *.pdb or *.pdbqt files.