As recently reported , these DW tools mimicked natural evolution, by generating thousands of children molecules fitting their crystallographic binding-cavities. However, ~ 50 % of the generated children predicted DW / Toxicity . More in detail, these co-evolutionary fast algorithms randomly generated tens of thousands of small molecular raw children from parent ligand molecules, rather than screening for a few hundreds in large chemical banks. Using DW-BEL, the abundant raw children were then rapidly ranked by best fitting both to their binding cavity and to other co-evolving criteria such as molecular weight and hydrophobicity . Best fitted thousands of evolved children could be generated in few hours for each protein / ligand pair. In collaboration with DW researchers, we explored here the inclusion of Toxicity Risk during DW-BEL coevolution to evaluate any possible improvements on the resulting children using nine protein / ligand crystallographic pairs as selected examples. To further finetune their accuracies, the DW-BEL top-lead docking-affinities were confirmed by the widely known AutoDockVina (ADV) algorithms.