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Nucleic Acids Res. 2014 Jul;42(Web Server issue):W26-31. doi: 10.1093/nar/gku477. Epub 2014 May 30.

SuperPred: update on drug classification and target prediction.

Author information

1
Charité-University Medicine Berlin, Structural Bioinformatics Group, Institute of Physiology & Experimental Clinical Research Center, Berlin 13125, Germany Charité-University Medicine Berlin, Division of General Pediatrics, Department of Pediatric Oncology and Hematology, Berlin 13353, Germany.
2
Charité-University Medicine Berlin, Structural Bioinformatics Group, Institute of Physiology & Experimental Clinical Research Center, Berlin 13125, Germany German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
3
Charité-University Medicine Berlin, Structural Bioinformatics Group, Institute of Physiology & Experimental Clinical Research Center, Berlin 13125, Germany Graduate School of Computational System Biology, Berlin 10115, Germany.
4
Charité-University Medicine Berlin, Structural Bioinformatics Group, Institute of Physiology & Experimental Clinical Research Center, Berlin 13125, Germany.
5
Charité-University Medicine Berlin, Structural Bioinformatics Group, Institute of Physiology & Experimental Clinical Research Center, Berlin 13125, Germany robert.preissner@charite.de.

Abstract

The SuperPred web server connects chemical similarity of drug-like compounds with molecular targets and the therapeutic approach based on the similar property principle. Since the first release of this server, the number of known compound-target interactions has increased from 7000 to 665,000, which allows not only a better prediction quality but also the estimation of a confidence. Apart from the addition of quantitative binding data and the statistical consideration of the similarity distribution in all drug classes, new approaches were implemented to improve the target prediction. The 3D similarity as well as the occurrence of fragments and the concordance of physico-chemical properties is also taken into account. In addition, the effect of different fingerprints on the prediction was examined. The retrospective prediction of a drug class (ATC code of the WHO) allows the evaluation of methods and descriptors for a well-characterized set of approved drugs. The prediction is improved by 7.5% to a total accuracy of 75.1%. For query compounds with sufficient structural similarity, the web server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. SuperPred is publicly available without registration at: http://prediction.charite.de.

PMID:
24878925
PMCID:
PMC4086135
DOI:
10.1093/nar/gku477
[Indexed for MEDLINE]
Free PMC Article

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