Two kinase pocket structures -encoded as two fingerprints -can be compared in two steps (Figure ). First, we calculate for each feature the distance between the corresponding two feature vectors across the 85 residue entries, producing a feature distances vector of length 15 (i.e., aggregating over the columns in Figure ). For example, the two fingerprints' 85-bit size feature vectors -representing the size of each of the 85 pocket residues -will be reduced to a single size feature distance. The distance between discrete features is defined as the scaled L1 norm x 1 = 1 n n i=1 |x i | (scaled Manhattan distance), whereas the distance between continuous features is defined as the scaled L2 norm