NNScore: a neural-network-based scoring function for the characterization of protein-ligand complexes

J Chem Inf Model. 2010 Oct 25;50(10):1865-71. doi: 10.1021/ci100244v.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Drug Design*
  • Humans
  • Ligands
  • Neural Networks, Computer*
  • Protein Binding
  • Proteins / metabolism*


  • Ligands
  • Proteins