Cargando…
SuperPred: drug classification and target prediction
The drug classification scheme of the World Health Organization (WHO) [Anatomical Therapeutic Chemical (ATC)-code] connects chemical classification and therapeutic approach. It is generally accepted that compounds with similar physicochemical properties exhibit similar biological activity. If this h...
Autores principales: | , , , , |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2008
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447784/ https://www.ncbi.nlm.nih.gov/pubmed/18499712 http://dx.doi.org/10.1093/nar/gkn307 |
_version_ | 1782156998710657024 |
---|---|
author | Dunkel, Mathias Günther, Stefan Ahmed, Jessica Wittig, Burghardt Preissner, Robert |
author_facet | Dunkel, Mathias Günther, Stefan Ahmed, Jessica Wittig, Burghardt Preissner, Robert |
author_sort | Dunkel, Mathias |
collection | PubMed |
description | The drug classification scheme of the World Health Organization (WHO) [Anatomical Therapeutic Chemical (ATC)-code] connects chemical classification and therapeutic approach. It is generally accepted that compounds with similar physicochemical properties exhibit similar biological activity. If this hypothesis holds true for drugs, then the ATC-code, the putative medical indication area and potentially the medical target should be predictable on the basis of structural similarity. We have validated that the prediction of the drug class is reliable for WHO-classified drugs. The reliability of the predicted medical effects of the compounds increases with a rising number of (physico-) chemical properties similar to a drug with known function. The web-server translates a user-defined molecule into a structural fingerprint that is compared to about 6300 drugs, which are enriched by 7300 links to molecular targets of the drugs, derived through text mining followed by manual curation. Links to the affected pathways are provided. The similarity to the medical compounds is expressed by the Tanimoto coefficient that gives the structural similarity of two compounds. A similarity score higher than 0.85 results in correct ATC prediction for 81% of all cases. As the biological effect is well predictable, if the structural similarity is sufficient, the web-server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. Availability: the system is freely accessible at http://bioinformatics.charite.de/superpred. SuperPred can be obtained via a Creative Commons Attribution Noncommercial-Share Alike 3.0 License. |
format | Text |
id | pubmed-2447784 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-24477842008-07-09 SuperPred: drug classification and target prediction Dunkel, Mathias Günther, Stefan Ahmed, Jessica Wittig, Burghardt Preissner, Robert Nucleic Acids Res Articles The drug classification scheme of the World Health Organization (WHO) [Anatomical Therapeutic Chemical (ATC)-code] connects chemical classification and therapeutic approach. It is generally accepted that compounds with similar physicochemical properties exhibit similar biological activity. If this hypothesis holds true for drugs, then the ATC-code, the putative medical indication area and potentially the medical target should be predictable on the basis of structural similarity. We have validated that the prediction of the drug class is reliable for WHO-classified drugs. The reliability of the predicted medical effects of the compounds increases with a rising number of (physico-) chemical properties similar to a drug with known function. The web-server translates a user-defined molecule into a structural fingerprint that is compared to about 6300 drugs, which are enriched by 7300 links to molecular targets of the drugs, derived through text mining followed by manual curation. Links to the affected pathways are provided. The similarity to the medical compounds is expressed by the Tanimoto coefficient that gives the structural similarity of two compounds. A similarity score higher than 0.85 results in correct ATC prediction for 81% of all cases. As the biological effect is well predictable, if the structural similarity is sufficient, the web-server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. Availability: the system is freely accessible at http://bioinformatics.charite.de/superpred. SuperPred can be obtained via a Creative Commons Attribution Noncommercial-Share Alike 3.0 License. Oxford University Press 2008-07-01 2008-05-22 /pmc/articles/PMC2447784/ /pubmed/18499712 http://dx.doi.org/10.1093/nar/gkn307 Text en © 2008 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Dunkel, Mathias Günther, Stefan Ahmed, Jessica Wittig, Burghardt Preissner, Robert SuperPred: drug classification and target prediction |
title | SuperPred: drug classification and target prediction |
title_full | SuperPred: drug classification and target prediction |
title_fullStr | SuperPred: drug classification and target prediction |
title_full_unstemmed | SuperPred: drug classification and target prediction |
title_short | SuperPred: drug classification and target prediction |
title_sort | superpred: drug classification and target prediction |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447784/ https://www.ncbi.nlm.nih.gov/pubmed/18499712 http://dx.doi.org/10.1093/nar/gkn307 |
work_keys_str_mv | AT dunkelmathias superpreddrugclassificationandtargetprediction AT guntherstefan superpreddrugclassificationandtargetprediction AT ahmedjessica superpreddrugclassificationandtargetprediction AT wittigburghardt superpreddrugclassificationandtargetprediction AT preissnerrobert superpreddrugclassificationandtargetprediction |