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Scientia Pharmaceutica is published by MDPI from Volume 84 Issue 3 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on as a courtesy and upon agreement with Austrian Pharmaceutical Society (Österreichische Pharmazeutische Gesellschaft, ÖPhG).
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Sci. Pharm. 2009, 77(Posters (PO)), 207;

SIBAR Descriptors and Support Vector Machine for ABCB1 Substrate Prediction

Department of Medicinal Chemistry, University of Vienna, Althanstraße 14, 1090, Vienna, Austria
Author to whom correspondence should be addressed.
Received: 16 April 2009 / Accepted: 16 April 2009 / Published: 16 April 2009
PDF [198 KB, uploaded 12 May 2017]


As part of the ATP-binding cassette transporter superfamily ABCB1 (P-gp) exports a multitude of xenobiotics and is strongly connected to multi-drug resistance (MDR). With the decreasing number of new drugs entering the market prediction of possible side effects in new lead structures gains increasing importance. In silico methods hereby have become highly significant. As crystal structures of membrane proteins are difficult to produce and a structure of ABCB1 has yet to be published, ligand based approaches are still the method of choice. The aim of this study is to compare two approaches widely used for substrate prediction.
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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SCHWAHA, R.; ECKER, G.F. SIBAR Descriptors and Support Vector Machine for ABCB1 Substrate Prediction. Sci. Pharm. 2009, 77, 207.

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