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Simultaneous prediction of four ATP-binding cassette transporters substrates using multi-label QSAR

Aniceto, Natália, Freitas, Alex A., Bender, Andreas, Ghafourian, Taravat (2016) Simultaneous prediction of four ATP-binding cassette transporters substrates using multi-label QSAR. Molecular Informatics, 35 (10). pp. 514-528. ISSN 1868-1751. (doi:10.1002/minf.201600036)

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http://dx.doi.org/10.1002/minf.201600036

Abstract

Efflux by the ATP-binding cassette (ABC) transporters affects the pharmacokinetic profile of drugs and it has been implicated in drug-drug interactions as well as its major role in multi-drug resistance in cancer. It is therefore important for the pharmaceutical industry to be able to understand what phenomena rule ABC substrate recognition. Considering a high degree of substrate overlap between various members of ABC transporter family, it is advantageous to employ a multi-label classification approach where predictions made for one transporter can be used for modeling of the other ABC transporters. Here, we present decision tree-based QSAR classification models able to simultaneously predict substrates and non-substrates for BCRP1, P-gp/MDR1 and MRP1 and MRP2, using a dataset of 1493 compounds. To this end, two multi-label classification QSAR modelling approaches were adopted: Binary Relevance (BR) and Classifier Chain (CC). Even though both multi-label models yielded similar predictive performances in terms of overall accuracies (close to 70?%), the CC model overcame the problem of skewed performance towards identifying substrates compared with non-substrates, which is a common problem in the literature. The models were thoroughly validated by using external testing, applicability domain and activity cliffs characterization. In conclusion, a multi-label classification approach is an appropriate alternative for the prediction of ABC efflux.

Item Type: Article
DOI/Identification number: 10.1002/minf.201600036
Uncontrolled keywords: data mining, machine learning, classification, QSAR
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 09 Mar 2017 18:09 UTC
Last Modified: 05 Sep 2019 08:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/60785 (The current URI for this page, for reference purposes)
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