NNScore: a neural-network-based scoring function for the characterization of protein-ligand complexes.
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.
Durrant
Jacob D
JD
Department of Chemistry & Biochemistry, NSF Center for Theoretical Biological Physics, National Biomedical Computation Resource, Howard Hughes Medical Institute, University of California San Diego, La Jolla, California 92093, USA. jdurrant@ucsd.edu
McCammon
J Andrew
JA
D015195:Drug Design; D006801:Humans; D008024:Ligands; D016571:Neural Networks, Computer; D011485:Protein Binding; D011506:Proteins / Q000378:metabolism*
D008024:Ligands; D011506:Proteins