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J Chem Inf Model. 2013 Jul 22;53(7):1726-35. doi: 10.1021/ci400042y. Epub 2013 Jul 11.

Comparing neural-network scoring functions and the state of the art: applications to common library screening.

Author information

1
Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA. jdurrant@ucsd.edu

Abstract

We compare established docking programs, AutoDock Vina and Schrödinger's Glide, to the recently published NNScore scoring functions. As expected, the best protocol to use in a virtual-screening project is highly dependent on the target receptor being studied. However, the mean screening performance obtained when candidate ligands are docked with Vina and rescored with NNScore 1.0 is not statistically different than the mean performance obtained when docking and scoring with Glide. We further demonstrate that the Vina and NNScore docking scores both correlate with chemical properties like small-molecule size and polarizability. Compensating for these potential biases leads to improvements in virtual screen performance. Composite NNScore-based scoring functions suited to a specific receptor further improve performance. We are hopeful that the current study will prove useful for those interested in computer-aided drug design.

PMID:
23734946
PMCID:
PMC3735370
DOI:
10.1021/ci400042y
[Indexed for MEDLINE]
Free PMC Article

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