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.
Keywords: Artificial neural network (ANN), breast cancer resistant protein (BCRP), k-nearest neighbor (k-NN), machine learning (ML), support vector machine (SVM).
Combinatorial Chemistry & High Throughput Screening
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.
Export Options
About this article
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
DOI https://dx.doi.org/10.2174/1386207318666150525094503 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
Call for Papers in Thematic Issues
Artificial Intelligence Methods for Biomedical, Biochemical and Bioinformatics Problems
Recently, a large number of technologies based on artificial intelligence have been developed and applied to solve a diverse range of problems in the areas of biomedical, biochemical and bioinformatics problems. By utilizing powerful computing resources and massive amounts of data, methods based on artificial intelligence can significantly improve the ...read more
Eco-friendly Agents for Biological Control of Pathogenic Diseases
The discovery of an alternative biological approach to disease management includes work on medicinal products derived from natural sources as a starting point for the development of eco-friendly agents for these diseases and the injuries they cause, as well as reducing human contact with hazardous chemicals and their residues. We ...read more
Emerging trends in diseases mechanisms, noble drug targets and therapeutic strategies: focus on immunological and inflammatory disorders
Recently infectious and inflammatory diseases have been a key concern worldwide due to tremendous morbidity and mortality world Wide. Recent, nCOVID-9 pandemic is a good example for the emerging infectious disease outbreak. The world is facing many emerging and re-emerging diseases out breaks at present however, there is huge lack ...read more
Exploring Spectral Graph Theory in Combinatorial Chemistry
Scope of the Thematic Issue: Combinatorial chemistry involves the synthesis and analysis of a large number of diverse compounds simultaneously. Traditional methods rely on brute force experimentation, which can be time-consuming and resource-intensive. Spectral Graph Theory, a branch of mathematics dealing with the properties of graphs in relation to the ...read more
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
Related Articles
-
Evaluation of BMP-2 Minicircle DNA for Enhanced Bone Engineering and Regeneration
Current Gene Therapy Molecular Mechanisms of Neuronal Histamine and its Receptors in Obesity
Current Molecular Pharmacology Immune Modulation by Regulatory T Cells in Helicobacter pylori-Associated Diseases
Endocrine, Metabolic & Immune Disorders - Drug Targets Medicinal Plants as a Source of New Therapeutic Products: Genus Mentha and the Potential Antimicrobial Activity of Extracts and Essential Oils
Current Traditional Medicine Advances in Systemic Therapy for Gastroenteropancreatic Neuroendocrine Malignancies
Current Clinical Pharmacology Immunonutrition in Surgical Patients
Current Drug Targets Connexins as Precocious Markers and Molecular Targets for Chemical and Pharmacological Agents in Carcinogenesis
Current Medicinal Chemistry Multiple Roles of Annexin A2 in Post-Transcriptional Regulation of Gene Expressio
Current Protein & Peptide Science Back to the Bench? MEK and ERK Inhibitors for the Treatment of KRAS Mutant Lung Adenocarcinoma
Current Medicinal Chemistry Signal Transduction Inhibitors as Radiosensitizers
Current Medicinal Chemistry - Anti-Cancer Agents Prognostic Markers in Small Cell Lung Cancer
Current Cancer Therapy Reviews Many Drugs and Phytochemicals Can Be Activated to Biological Reactive Intermediates
Current Drug Metabolism Synthesis, Characterization, Anti-proliferative Evaluation, and DNA Flow Cytometry Analysis of Some 2-Thiohydantoin Derivatives
Mini-Reviews in Medicinal Chemistry Subject Index To Volume 9
Combinatorial Chemistry & High Throughput Screening Relationship Between Metal Transcription Factor-1 and Zinc in Resistance to Metals Producing Free Radicals
Current Chemical Biology Peptide Based Vaccine Design for Cancer Immunotherapy
Letters in Drug Design & Discovery Regulation of Multidrug Resistance by Pro-Inflammatory Cytokines
Current Cancer Drug Targets Molecular Treatment of Different Breast Cancers
Anti-Cancer Agents in Medicinal Chemistry Retracted: Potential Health Benefits of Broccoli- A Chemico-Biological Overview
Mini-Reviews in Medicinal Chemistry Gene Silencing in the Development of Personalized Cancer Treatment: The Targets, the Agents and the Delivery Systems
Current Gene Therapy