Machine learning-based scoring systems have been proposed as substitutes to improve the prediction performance of conventional scoring systems. RF-Score is a prominent machine learning-based grading system ( ). The present approach quantifies intermolecular interactions by use of atom pair counting comprising nine different heavy-atom types: C, N, O, F, P, S, Cl, Br, and I. Evaluated on the PDBbind ( ) v2007 benchmark set, the RF-Score shows significant improvement over the existing techniques. Moreover, among the six additional parameters of the RF-Score v3 model ( ), it shows better prediction accuracy than the original model. As described in reference ( ), the Structural Interaction Fingerprints (SIFt) method is a machine learning technique using a format akin to fingerprints to document intermolecular interactions.