Abstract
P2RANK is a novel machine learning-based method for prediction of ligand binding sites from protein structure. P2RANK uses Random Forests classifier to infer ligandability of local chemical neighborhoods near the protein surface which are represented by specific near-surface points and described by aggregating physico-chemical features projected on those points from neighboring protein atoms.
The points with high predicted ligandability are clustered and ranked to obtain the resulting list of binding site predictions.