Classification of Breast Cancer Resistant Protein (BCRP) Inhibitors and Non-Inhibitors Using Machine Learning Approaches

Title:Classification of Breast Cancer Resistant Protein (BCRP) Inhibitors and Non-Inhibitors Using Machine Learning Approaches

Volume: 18 Issue: 5

Author(s): Vilas Belekar, Karthik Lingineni and Prabha Garg

Affiliation:

Keywords: Artificial neural network (ANN), breast cancer resistant protein (BCRP), k-nearest neighbor (k-NN), machine learning (ML), support vector machine (SVM).

Abstract: 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.

Cite this article as:

Belekar Vilas, Lingineni Karthik and Garg Prabha, Classification of Breast Cancer Resistant Protein (BCRP) Inhibitors and Non-Inhibitors Using Machine Learning Approaches, Combinatorial Chemistry & High Throughput Screening 2015; 18 (5) . https://dx.doi.org/10.2174/1386207318666150525094503