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Sci. Pharm. 2009, 77(Posters (PO)), 207; doi:10.3797/scipharm.oephg.21.PO-08 (registering DOI)

SIBAR Descriptors and Support Vector Machine for ABCB1 Substrate Prediction

Department of Medicinal Chemistry, University of Vienna, Althanstraße 14, 1090, Vienna, Austria
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Received: 16 April 2009 / Accepted: 16 April 2009 / Published: 16 April 2009
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Abstract

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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MDPI and ACS Style

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

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