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

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.

journal_name

Comb Chem High Throughput Screen

journal_title

Combinatorial chemistry & high throughput screening

authors

Belekar V,Lingineni K,Garg P

doi

10.2174/1386207318666150525094503

subject

Has Abstract

pub_date

2015-01-01 00:00:00

pages

476-85

issue

5

eissn

1386-2073

issn

1875-5402

pii

CCHTS-EPUB-67603

journal_volume

18

pub_type

杂志文章

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