To score a protein-ligand interaction using a KNN classifier, we must first define a single similarity measure over protein and ligand pairs. If L is the ligand Tanimoto similarity and R is the protein pocket TM score between two datapoints, we want to know the optimal linear combination of these scores to compute the total similarity. To do so, we estimate the joint probability distribution P (L, R) for all pairs of datapoints in BigBind. We then fit a linear model to predict log P (L,R) P (L)P (R) . This ratio measures how likely two ligand and protein similarities are to co-occur relative to what would be expected from random co-occurrence.