Similarity Based Descriptors - Useful for Classification of Substrates of the Human Multidrug Transporter P-Glycoprotein?

• Published in: QSAR & combinatorial science 2009-08, Vol.28 (8), p.834-839
• Main Authors: Schwaha, Rita, Ecker, Gerhard F.
• Format: Article
• Language: English
• Subjects: Binary QSAR; Classification of substrates; Medicinal chemistry; P-glycoprotein; Similarity based descriptors; Support vector machine
• ISSN: 1611-020X; 1611-0218
• DOI: 10.1002/qsar.200960051

As part of the ATP‐binding cassette transporter superfamily P‐glycoprotein (ABCB1) acts as xenotoxic exporter and consequently is strongly involved in multidrug resistance (MDR) and drug‐drug interactions. In this work we focus on our in‐house developed SIBAR approach for prediction of ABCB1 substrates. SIBAR values were calculated on basis of three different descriptor sets: 2D‐MOE descriptors, VSA descriptors and 3D Autocorrelation vectors using in total four reference sets. In order to compare linear with non‐linear classification methods we used binary QSAR and a support vector machine (SVM), respectively. Results demonstrate that with 2D‐MOE and VSA‐descriptors prediction of non substrates performs better, whereas autocorrelation vectors show higher accuracy for substrates. With respect to the different reference sets used in this case selection on basis of maximum diversity yielded better results than a set derived from the training set compounds. In general, the models show distinct differences in their performance depending on the combination of method and descriptor type.