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Computational docking is the core process of computer-aided drug design; itaims at predicting the best orientation and conformation of a small drugmolecule when bound to a target large protein receptor. The docking quality istypically measured by a scoring function: a mathematical predictive model thatproduces a score representing the binding free energy and hence the stabilityof the resulting complex molecule. We analyze the performance of both learningtechniques on the scoring power, the ranking power, docking power, andscreening power using the PDBbind 2013 database. For the scoring and rankingpowers, the proposed learning scoring functions depend on a wide range offeatures (energy terms, pharmacophore, intermolecular) that entirelycharacterize the protein-ligand complexes. For the docking and screeningpowers, the proposed learning scoring functions depend on the intermolecularfeatures of the RF-Score to utilize a larger number of training complexes. Forthe scoring power, the DL\_RF scoring function achieves Pearson's correlationcoefficient between the predicted and experimentally measured bindingaffinities of 0.799 versus 0.758 of the RF scoring function. For the rankingpower, the DL scoring function ranks the ligands bound to fixed target proteinwith accuracy 54% for the high-level ranking and with accuracy 78% for thelow-level ranking while the RF scoring function achieves (46% and 62%)respectively. For the docking power, the DL\_RF scoring function has a successrate when the three best-scored ligand binding poses are considered within 2\AA\ root-mean-square-deviation from the native pose of 36.0% versus 30.2% ofthe RF scoring function. For the screening power, the DL scoring function hasan average enrichment factor and success rate at the top 1% level of (2.69 and6.45%) respectively versus (1.61 and 4.84%) respectively of the RF scoringfunction.

Deep learning is competing random forest in computational docking