Abstract
We present the results of an extensive computational study in which we show that combining scoring functions in an intersection-based consensus approach results in an enhancement in the ability to discriminate between active and inactive enzyme inhibitors. This is illustrated in the context of docking collections of three-dimensional structures into three different enzymes of pharmaceutical interest: p38 MAP kinase, inosine monophosphate dehydrogenase, and HIV protease. An analysis of two different docking methods and thirteen scoring functions provides insights into which functions perform well, both singly and in combination. Our data shows that consensus scoring further provides a dramatic reduction in the number of false positives identified by individual scoring functions, thus leading to a significant enhancement in hit-rates.
Keywords
Docking (animal)Computational biologyChemistryFalse positive paradoxComputer scienceData miningArtificial intelligenceBiology
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Publication Info
- Year
- 1999
- Type
- article
- Volume
- 42
- Issue
- 25
- Pages
- 5100-5109
- Citations
- 682
- Access
- Closed
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Cite This
Paul S. Charifson,
Joseph J. Corkery,
Mark A. Murcko
et al.
(1999).
Consensus Scoring: A Method for Obtaining Improved Hit Rates from Docking Databases of Three-Dimensional Structures into Proteins.
Journal of Medicinal Chemistry
, 42
(25)
, 5100-5109.
https://doi.org/10.1021/jm990352k
Identifiers
- DOI
- 10.1021/jm990352k