%0 Journal Article %1 Durrant2010LigandNeuralNetwork %A Durrant, Jacob D. %A McCammon, J. Andrew %D 2010 %J Journal of Chemical Information and Modeling %K computational-biology ligand-binding ligand-docking neural-networks virtual-screening %N 10 %P 1865-1871 %R 10.1021/ci100244v %T NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes %U /brokenurl# http://dx.doi.org/10.1021/ci100244v %V 50 %X 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.

@article{Durrant2010LigandNeuralNetwork, 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. }, added-at = {2016-03-25T20:28:40.000+0100}, author = {Durrant, Jacob D. and McCammon, J. Andrew}, biburl = {https://www.bibsonomy.org/bibtex/29024a41b1f119526129b433ae6f63024/salotz}, description = {NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes - Journal of Chemical Information and Modeling (ACS Publications)}, doi = {10.1021/ci100244v}, eprint = {http://dx.doi.org/10.1021/ci100244v}, interhash = {9254e763e1c381ea66a6618e0c4482c6}, intrahash = {9024a41b1f119526129b433ae6f63024}, journal = {Journal of Chemical Information and Modeling}, keywords = {computational-biology ligand-binding ligand-docking neural-networks virtual-screening}, note = {PMID: 20845954}, number = 10, pages = {1865-1871}, timestamp = {2016-03-25T20:28:40.000+0100}, title = {NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes}, url = {/brokenurl# http://dx.doi.org/10.1021/ci100244v }, volume = 50, year = 2010 }