Background: Breast Cancer Resistance Protein (BCRP or ABCG2) is a polyspecific effluxtransporter
which belongs to the ATP-binding Cassette superfamily. Up-regulation of BCRP is
associated to multi-drug resistance in a number of conditions, e.g. cancer and epilepsy. Recent
proteomic studies show that high expression levels of BCRP are found in healthy human intestine and at
the blood-brain barrier, limiting the absorption and brain distribution of its substrates. Therefore, the
early recognition of BCRP substrates seems to be crucial in the early phase of drug discovery.
Objective: The development of computational models that allow the early detection of BCRP substrates
Method: We have jointly applied the Enhanced Replacement Method and ensemble learning approaches
to obtain combinations of 2D linear classifiers capable of discriminating among substrates and nonsubstrates
of the wild type human BCRP.
Results: The ensemble learning approach combining the 10-Enhanced Replacement Method best
individual models obtained through MAX Operator displayed the best ability to discriminate between
BCRP substrates and non-substrates across all the validation sets/libraries used.
Conclusion: The best model ensemble obtained outperforms previously reported 2D linear classifiers,
showing the ability of the Enhanced Replacement Method and ensemble learning schemes to optimize
the performance of individual models. This is the first application of the Enhanced Replacement
Method to solve classification problems.