The proposed no-toxic co-evolution has enriched the number of candidates predicting higher affinities and specificities in only a few hours of computation (mostly due to the fast Java's code). All these would had been impossible for any other more "traditional" computational screening (i.e., AutoDockVina, Yasara, seeSAR, etc) of largest chemical libraries (i.e., Mcule, ChemSpace, Zinc, PubChem, Chembl, etc) which would have required much more computational time. On the other hand, most actual machine-learning approaches to docking ; including those employing the new transformer methods , are actually limited in their accuracies because of the reduced numbers of examples of small-drugs protein interactions required for model training . Some hands-on experience of the state-of-the-art of machine-learning trained models, was acquired by employing Hots proposed methods Hots predicted both docking-cavities and ligand docking-scores solely protein amino acid sequences . Despite Hots successful identification of the Vkorc1 binding cavity, Hots predicted only one possible ligand candidate. When forced to predict 150 ligand docking-scores 3 , several days were required to computational complete the docking. Furthermore, the generated docking-scores correlated poorly when compared to DW or ADV docking programs (results not shown). It may be concluded that programs by machine learning as docking alternatives are still on development. In contrast, the alternative random generation / selection algorithms proposed here, could generate higher numbers of candidates much faster and including Toxicity Risk (and/or other criteria) during their co-evolution (libraries-ondemand or generative biology by "co-evolutionary docking"). The question may remain as to whether or not, any evolutionary approach similar to the one described here, could be hybridized with any machine-learning for further improvements. Similar "hybrid" methods may be most important to predict proteinprotein predictions .