NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes
Citations Over TimeTop 10% of 2010 papers
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 scoring function presented here, used either on its own or in conjunction with other more traditional functions, could prove useful in future drug-discovery efforts.
Related Papers
- → RO Membrane Characterization(2018)25 cited
- → Introduction and Perspectives(2012)2 cited
- → Materials Characterization in Continuous Fiber-Reinforced Ceramic Composites Served in Simulating Environment(2007)
- → Natural Polymer‐Based Biomaterials and its Properties(2017)
- → Introduction and Overview of Materials Characterization(1998)