Additional co-evolution preference criteria were, fitting the cavity using a relative maximal weight of 4, MW <= 600 g/mol (weight 2) and hydrophobicity LogP <=4 (weight 1) (to limit children unspecificities) and Toxicity risk <=1 (weight 4) (to minimize toxicity risks during co-evolution). To perform DWBEL the optimal mmff94s+ force-field algorithm was chosen. The DWBEL co-evolution generations randomly added/inserted small molecular variations into the 2D cn30 to originate tens of thousands of raw-children that were consecutively numbered (ID) (Figure ). Each raw-children was evaluated for fitness not only to the provided target cavity, but also to the user-defined criteria. The DWBEL evaluation discarded the non-fitting and selected the best-fitted children DWBEL-conformers. The processes were repeated during several runs of hundreds of generations per run, to yield hundreds of unique non-toxic bestfitted children DWBEL-conformers with increased cavity-specificities . To prepare for ADV docking, the non-toxic fitted-children were further filtered to exclude any mutagenesis, tumorigenicity, reproductive interference, irritant molecular signatures, and/or nasty functions that had survived the co-evolution, by screening hundreds of those toxic possibilities36. The final output of hundreds of non-toxic fitted-children DWBEL-conformers ordered by their generation were renumbered by their affinities (NN) and saved as *.sdf 3D files including their mmff94s+ minimization conformers to preserve their 2D geometries. This minimization step proved to be essential for optimal PyMol visualization (using the split_states PyMol command) and to increase the accuracy and reproducibility of ADV docking .