Thousands of unique raw-children candidates were randomly generated from the previously identified ADV blind-docking L and C by using DataWarrior-Build Evolutionary Library (DWBEL). Once supplied with each of the L / C inputs, co-evolutions were performed with the same preference 4 criteria. Thus, relative maximal importance for docking-score of 4 (x4), molecular weight <= 600 g/mol (x2), hydrophobicity LogP <=4 (x1) and Toxicity risk <=1 (x4) (Supplementary Materials / DWBELhowto.mp4). The DWBEL co-evolution generation iterations randomly added/inserted small molecular variations into the L to originate tens of thousands of consecutively numbered raw-children (ID). The ID number, therefore, corresponds to the number of rawchildren generated before finding each fitted-children conformer. Using the optimal mmff94s+ force-field algorithm to generate the best conformers, each raw-children conformer was evaluated for fitness to the 4 criteria co-evolution. DWBEL selected the non-toxic best-fitted children conformers as output. To prepare for ADV docking, the non-toxic fitted-children were further filtered with a macro designed to exclude any survivor molecules with remaining toxicity preferences for mutagenesis, tumorigenicity, reproductive interference, irritant molecular signatures, and/or nasty functions explored by screening hundreds of those known motifs 12, 15, 16, 51 . The non-toxic fitted-and filtered children were ordered and NN numbered from low to high DWBEL docking-scores (high to low affinities) and finally saved as *.sdf 3D files to be visualized in DW and/or PyMol (Supplementary Materials / DWBEL-1448predictions.mp4 and DWBEL-PyMOLpredictions.mp4). The final *.sdf file included their corresponding mmff94s+ minimization conformers 37 required to preserve their 2D geometries for optimal PyMol visualization (using the split_states PyMol command) and/or to increase the accuracy and reproducibility of ADV docking .