Deep Learning is Competing with Random Forest in Computational Docking

Abstract

In this paper, we assess the scoring, ranking, docking, and screening powers of deep learning and random forest scoring functions. For the scoring power, the DL_RF scoring function (arithmetic mean of the DL and RF scores) achieves Pearson's correlation coefficient between the predicted and experimentally measured binding affinities of 0.799 versus 0.758 of the RF scoring function. For the ranking power, the DL scoring function ranks the ligands bound to fixed target protein with accuracy 54% for the high-level ranking (correctly ranking the three ligands bound to the same target protein in a cluster) and with accuracy 78% for the low-level ranking (correctly ranking the best ligand only in the cluster) while the RF scoring function achieves (46 % and 62%) respectively. For the docking power, the DL_RF scoring function has a success rate when the three best-scored ligand binding poses are considered within 2 A root-mean-square-deviation from the native pose of 36.0% versus 30.2% of the RF scoring function. For the screening power, the DL scoring function has an average enrichment factor and success rate at the top 1 % level of (2.69 and 6.45 %) respectively versus (1.61 and 4.84 %) respectively of the RF scoring function.