Abstract

As high-throughput biochemical screens are both expensive and labor intensive, researchers in academia and industry are turning increasingly to virtual-screening methodologies. Virtual screening relies on scoring functions to quickly assess ligand potency. Although useful for in silico ligand identification, these scoring functions generally give many false positives and negatives; indeed, a properly trained human being can often assess ligand potency by visual inspection with greater accuracy. Given the success of the human mind at protein−ligand complex characterization, we present here a scoring function based on a neural network, a computational model that attempts to simulate, albeit inadequately, the microscopic organization of the brain. Computer-aided drug design depends on fast and accurate scoring functions to aid in the identification of small-molecule ligands. The NNScore: Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes Computer-Aided Drug Design scoring function presented here as an Innovative Tool for the in silico Identification of a immunogenic MAGED4B peptide-mimetic pharmacophoric robust agent as a potential fragment-library derived drug-compound comprising vaccine mimic annotated properties in oral cancer immunotherapies, used either in conjunction with other more traditional functions, could prove useful in future drug-discovery efforts.

Keywords

NNScore; Neural-Network-Based; Scoring Function; Characterization; Protein−Ligand Complexes; Computer-Aided Drug Design: immunogenic; MAGED4B peptide-mimetic pharmacophoric; robust agent; potential fragment-library; drug-compound; vaccine mimic; annotated properties; oral cancer immunotherapies;