Although various standalone residue reactivity prediction algorithms take key ligandability features such as SASA and flexibility into account in detecting reactive residues, our experience suggests that protein conformation sampling and/or separate assessment for the presence a binding pocket in the vicinity of the predicted reactive residue site may be necessary for accurate results. Although the differentiating advantage of such an approach can be sensed from the data presented above, we further pursued a case in which we compared prediction performance of a purely computational method on a list of cysteine sites buried in an occluded binding pockets. A list of 47 apo-covPDB structures empirically confirmed to contain a buried ligandable cysteine covalent site were selected and tested for ligandability prediction by DeepCoSI 32 (apo-covPDBs_buriedCys, SupplementaryFile1). DeepCoSI performs global analysis of all cysteine residues on each query PDB file and ranks the cysteines according to their "ligandability" score.