We compared both the rank ordering and linear correlation of the five models we generated for the PDZ3 domain (three from our ML module and two from our statistical analysis module) by constructing a scatter matrix of the per residue scores (Figure ). Whilst the scatter matrix demonstrated all methods to be positively correlated with one another (Figure ), there were clear differences in the results obtained between the methods, with the CatBoost 73 ML algorithm seeming to give rise to the most different per residue scores between the methods tested (at least according to both metrics used, see Figure ). In terms of the top ranking residues, all five approaches consistently identified the residues G330, I336, R318 (classified as allosteric by Faure et al., ) and H372 (classified as orthosteric by Faure et al., ) as highly important (Figure ). Further, by comparing the closest heavy atom distance of each residue to any peptide residue, we observed that all five approaches were able to identify residues that were distal to the peptide binding site, but considered important (Figure ).