Abstract
Identification of potential druggable targets utilizing protein-protein interactions network (PPIN) has been emerging as a hotspot in drug discovery and development research. However, it remains unclear whether the currently used PPIN topological properties are enough to discriminate the drug targets from non-drug targets. In this study, three-step classification models using different network topological properties were designed and implemented using support vector machine (SVM) to compare the enrichment of known drug targets from non-targets. Surprisingly, none of the models was able to identify more than 75% of the true targets in the test set. It appears that the currently used simple PPIN topological properties are not likely robust enough for prediction of potential drug targets with high confidence, which also echoes similarly unsatisfying prediction data reported previously. However, we proposed that quality and quantity improvement of the protein-protein interactions (PPI) data for model training will help increasing the prediction accuracy.
Keywords: Drug targets, protein-protein interactions network, support vector machine, network topological properties.
Combinatorial Chemistry & High Throughput Screening
Title:Are Topological Properties of Drug Targets Based on Protein-Protein Interaction Network Ready to Predict Potential Drug Targets?
Volume: 19 Issue: 2
Author(s): Shiliang Li, Xiaojuan Yu, Chuanxin Zou, Jiayu Gong and Xiaofeng Liu
Affiliation:
Keywords: Drug targets, protein-protein interactions network, support vector machine, network topological properties.
Abstract: Identification of potential druggable targets utilizing protein-protein interactions network (PPIN) has been emerging as a hotspot in drug discovery and development research. However, it remains unclear whether the currently used PPIN topological properties are enough to discriminate the drug targets from non-drug targets. In this study, three-step classification models using different network topological properties were designed and implemented using support vector machine (SVM) to compare the enrichment of known drug targets from non-targets. Surprisingly, none of the models was able to identify more than 75% of the true targets in the test set. It appears that the currently used simple PPIN topological properties are not likely robust enough for prediction of potential drug targets with high confidence, which also echoes similarly unsatisfying prediction data reported previously. However, we proposed that quality and quantity improvement of the protein-protein interactions (PPI) data for model training will help increasing the prediction accuracy.
Export Options
About this article
Cite this article as:
Li Shiliang, Yu Xiaojuan, Zou Chuanxin, Gong Jiayu and Liu Xiaofeng, Are Topological Properties of Drug Targets Based on Protein-Protein Interaction Network Ready to Predict Potential Drug Targets?, Combinatorial Chemistry & High Throughput Screening 2016; 19(2) . https://dx.doi.org/10.2174/1386207319666151110122145
DOI https://dx.doi.org/10.2174/1386207319666151110122145 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |

- 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
- Announcements
- Forthcoming Thematic Issues
Related Articles
-
Proteomic Profiling of Maternal Serum for Early Risk Analysis of
Preterm Birth
Current Proteomics Pharmacological and Biological Activities of Xanthones
Anti-Infective Agents in Medicinal Chemistry Anticancer Drug Discovery Targeting DNA Hypermethylation
Current Medicinal Chemistry Lipid Nanocarriers and Molecular Targets for Malaria Chemotherapy
Current Drug Targets Adhatoda vasica Nees: Phytochemical and Pharmacological Profile
The Natural Products Journal Small Molecules Modulating Biogenesis or Processing of microRNAs with Therapeutic Potentials
Current Medicinal Chemistry Essential Oils with Microbicidal and Antibiofilm Activity
Current Pharmaceutical Biotechnology Recent Advances Using Phosphodiesterase 4 (PDE4) Inhibitors to Treat Inflammatory Disorders: Animal and Clinical Studies
Current Drug Therapy Molecular Determinants of Vascular Calcification: A Bench to Bedside View
Current Molecular Medicine Dendritic Cells: A Double-Edge Sword in Atherosclerotic Inflammation
Current Pharmaceutical Design Microbial Biotransformation: Recent Developments on Steroid Drugs
Recent Patents on Biotechnology Effects of Alcohol in the Lung
Current Respiratory Medicine Reviews Hybrid Molecules Development: A Versatile Landscape for the Control of Antifungal Drug Resistance: A Review
Mini-Reviews in Medicinal Chemistry Recent Advances in Metabolomics
Current Metabolomics Treasures Hunt in Old Mines: Terminalia chebula-Based Traditional Herbal Medicinal Products
The Natural Products Journal Drug Repurposing: An Emerging Tool for Drug Reuse, Recycling and Discovery
Current Drug Research Reviews Sterol 14α-Demethylase from Trypanosomatidae Parasites as a Promising Target for Designing New Antiparasitic Agents
Current Topics in Medicinal Chemistry Clinical Presentations and Diagnosis of Brucellosis
Recent Patents on Anti-Infective Drug Discovery Tofacitinib, an Oral Janus Kinase Inhibitor: Perspectives in Dermatology
Current Medicinal Chemistry The Multiple Roles of Vitamin D in Human Health. A Mini-Review
Immunology, Endocrine & Metabolic Agents in Medicinal Chemistry (Discontinued)