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Cited by 259

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes

Open access
Durrant, Jacob D., McCammon, J. Andrew·2010·Journal of Chemical Information and Modeling
DOI 10.1021/ci100244v
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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.

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Metadata

Authors
Durrant, J. D., & McCammon, J. A.
Venue
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Published
2010-09-16
Volume
50
Issue
10
Pages
1865-1871
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
DOI
10.1021/ci100244v
OpenAlex
W2030286884
Cited by
259
References
31
FWCI
7.12
Type
journal article
Language
en
Open access
hybrid

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