Journal
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume 50, Issue 10, Pages 1865-1871Publisher
AMER CHEMICAL SOC
DOI: 10.1021/ci100244v
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Funding
- UCSD School of Medicine
- NIH [GM31749]
- NSF [MCB-0506593, MCA93S013]
- Howard Hughes Medical Institute
- National Center for Supercomputing Applications
- San Diego Supercomputer Center
- W. M. Keck Foundation
- National Biomedical Computational Resource
- Center for Theoretical Biological Physics
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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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