How the co-evolution conformers were generated in H1R? As previously described, two inputs were required for DWBEL 9, 13 , i) one parent antihistamine 2D molecule (to generate raw-children) and ii) a docking cavity in H1R complexed with an antihistamine (to select bestfitting children). Thousands of unique children candidates were randomly generated from the antihistamine parents using DataWarrior-Build Evolutionary Library (DWBEL) co-evolution algorithms. Once supplied with one input parent / cavity, co-evolutions were performed during 3 consecutive runs with the same preference criteria, relative maximal importance for docking-score of 4 (x4), molecular weight <= 600 g/mol (x2), hydrophobicity LogP <=4 (x1) and Toxicity risk <=1 (x4). DWBEL co-evolution iterations randomly added/inserted small molecular variations into the 2D parents to originate tens of thousands of raw-children consecutively numbered by an ID. Therefore the ID number corresponds to the number of raw-children generated before each fitted-children conformer using the mmff94s+ force-field algorithm . To prepare for ADV docking, the non-toxic fitted-children were ranked by DWBEL docking-score and NN numbered and filtered by a DW macro (NTN) excluding any molecules with remaining mutagenesis, tumorigenicity, reproductive interference, irritant molecular signatures, and/or nasty hundred motifs 9, 12, 13, 34 . The non-toxic fitted-and filtered children were saved as *.sdf 3D files. Sdf files included the mmff94s+ minimization conformers required to preserve 2D geometries for optimal visualization (using the split_states PyMol command) and/or to increase accuracy and reproducibility of ADV docking .