Pending on solved crystallographic F13L structures, several docking cavities were identified as possible F13L new docking targeting candidates by blind-docking. Furthermore, to complicate F13L docking interpretations, different binding cavities may co-exist or be exchanged by mutations during one MPXV human outbreak, even within the same patient. Because of these docking-cavity uncertainties, we also explored some of most recent machine-learning new algorithms using transformers 78, 91, 56-58 relying only on protein amino acid sequence rather than docking cavities . Among all those that were tested, the Highlights on Target Sequences (HoTS), was the most reproducible and successful method to predict docking cavities 91 when applied to F13L (data not shown). Trained with only 232 protein-ligand known complexes, HoTS claimed a successful prediction of ~ 66 % binding cavities from not-seen-before protein sequences 91, 61 , despite the protein-ligand pair limitations for model training . In our hands, HoTS predicted some of the F13L docking-cavities described before and here (data not shown). However, HoTS was slow when compared to the speed at which thousands / hundreds of ligand children were generated by DWBEL. It appears that sequence-only docking methods would still require more development efforts .