The breast cancer resistant protein (BCRP) is an important transporter and its inhibitors play
an important role in cancer treatment by improving the oral bioavailability as well as blood brain
barrier (BBB) permeability of anticancer drugs. In this work, a computational model was developed to
predict the compounds as BCRP inhibitors or non-inhibitors. Various machine learning approaches
like, support vector machine (SVM), k-nearest neighbor (k-NN) and artificial neural network (ANN)
were used to develop the models. The Matthews correlation coefficients (MCC) of developed models
using ANN, k-NN and SVM are 0.67, 0.71 and 0.77, and prediction accuracies are 85.2%, 88.3% and
90.8% respectively. The developed models were tested with a test set of 99 compounds and further validated with external
set of 98 compounds. Distribution plot analysis and various machine learning models were also developed based on druglikeness
descriptors. Applicability domain is used to check the prediction reliability of the new molecules.
Keywords: Artificial neural network (ANN), breast cancer resistant protein (BCRP), k-nearest neighbor (k-NN), machine
learning (ML), support vector machine (SVM).
Rights & PermissionsPrintExport