Abstract
Cancer is one of the major causes of death in human beings. While traditional cancer treatments kill cancerous cells, they negatively affect normal cells. In addition, the side effects and high medical costs of treatment prevent effective management of cancer. Nonetheless, anticancer peptides have gained popularity over the recent years as potential therapeutic agents that may complement traditional therapies. Compared to conventional wet-lab experiments, computation-based methods provide a promising platform for high-throughput identification of peptides that have anticancer activity. Therefore, this review summarizes the currently available databases for anticancer peptides/proteins. This is a survey of 22 recently published in-silico methods that aim to predict anticancer peptides accurately. More specifically, the article details the benchmark datasets, feature construction, feature selection, machine learning algorithms, assessment criteria, comparison of different methods, and publicly available predictors. We also compare the prediction performance of these predictors to the benchmark dataset. Finally, the study makes several recommendations concerning the future development of databases for anticancer peptides and methods that can be used to predict anticancer peptides.
Keywords: Anticancer peptide, In-silico, Machine learning, Feature construction, Feature selection, Cancer.
Current Topics in Medicinal Chemistry
Title:Survey of In-silico Prediction of Anticancer Peptides
Volume: 21 Issue: 15
Author(s): Nan Ye*
Affiliation:
- School of Finance and Economics, Xinyang Agriculture and Forestry University, Xinyang 464000,China
Keywords: Anticancer peptide, In-silico, Machine learning, Feature construction, Feature selection, Cancer.
Abstract: Cancer is one of the major causes of death in human beings. While traditional cancer treatments kill cancerous cells, they negatively affect normal cells. In addition, the side effects and high medical costs of treatment prevent effective management of cancer. Nonetheless, anticancer peptides have gained popularity over the recent years as potential therapeutic agents that may complement traditional therapies. Compared to conventional wet-lab experiments, computation-based methods provide a promising platform for high-throughput identification of peptides that have anticancer activity. Therefore, this review summarizes the currently available databases for anticancer peptides/proteins. This is a survey of 22 recently published in-silico methods that aim to predict anticancer peptides accurately. More specifically, the article details the benchmark datasets, feature construction, feature selection, machine learning algorithms, assessment criteria, comparison of different methods, and publicly available predictors. We also compare the prediction performance of these predictors to the benchmark dataset. Finally, the study makes several recommendations concerning the future development of databases for anticancer peptides and methods that can be used to predict anticancer peptides.
Export Options
About this article
Cite this article as:
Ye Nan *, Survey of In-silico Prediction of Anticancer Peptides, Current Topics in Medicinal Chemistry 2021; 21 (15) . https://dx.doi.org/10.2174/1568026621666210612030536
DOI https://dx.doi.org/10.2174/1568026621666210612030536 |
Print ISSN 1568-0266 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4294 |
Call for Papers in Thematic Issues
Chemistry Based on Natural Products for Therapeutic Purposes
The development of new pharmaceuticals for a wide range of medical conditions has long relied on the identification of promising natural products (NPs). There are over sixty percent of cancer, infectious illness, and CNS disease medications that include an NP pharmacophore, according to the Food and Drug Administration. Since NP ...read more
Current Trends in Drug Discovery Based on Artificial Intelligence and Computer-Aided Drug Design
Drug development discovery has faced several challenges over the years. In fact, the evolution of classical approaches to modern methods using computational methods, or Computer-Aided Drug Design (CADD), has shown promising and essential results in any drug discovery campaign. Among these methods, molecular docking is one of the most notable ...read more
Drug Discovery in the Age of Artificial Intelligence
In the age of artificial intelligence (AI), we have witnessed a significant boom in AI techniques for drug discovery. AI techniques are increasingly integrated and accelerating the drug discovery process. These developments have not only attracted the attention of academia and industry but also raised important questions regarding the selection ...read more
From Biodiversity to Chemical Diversity: Focus of Flavonoids
Flavonoids are the largest group of polyphenols, plant secondary metabolites arising from the essential aromatic amino acid phenylalanine (or more rarely from tyrosine) via the phenylpropanoid pathway. The flavan nucleus is the basic 15-carbon skeleton of flavonoids (C6-C3-C6), which consists of two phenyl rings (A and B) and a heterocyclic ...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
- Announcements
Related Articles
-
Mesenchymal Stem Cells: Use in Cartilage Repair
Current Rheumatology Reviews Applications of Lipid-based Nanocarriers for Parenteral Drug Delivery
Current Medicinal Chemistry Hypertension in Pregnancy: Clinical Manifestations and Treatment
Current Hypertension Reviews A Prevention of Pre-eclampsia with the Use of Acetylsalicylic Acid and Low-molecular Weight Heparin – Molecular Mechanisms
Current Pharmaceutical Biotechnology Arterial Stiffness and Cardiovascular Drugs
Current Pharmaceutical Design The Impact of Cardiovascular Diseases on Cardiovascular Regulation During Exercise in Humans: Studies on Metaboreflex Activation Elicited by the Post-exercise Muscle Ischemia Method
Current Cardiology Reviews Glucagon-Like Peptide 1 and the Cardiovascular System
Current Diabetes Reviews Advances in Drug Safety
Current Pharmaceutical Design An Overview of Data Mining Algorithms in Drug Induced Toxicity Prediction
Mini-Reviews in Medicinal Chemistry Renal Artery Stenosis: Current Perspectives on Imaging and Endovascular Management
Current Hypertension Reviews In Vivo Measurement in Pigs of Wash-In Kinetics of Xenon at its Site of Action
Current Clinical Pharmacology Computational Modeling Approaches for Studying of Synthetic Biological Networks
Current Bioinformatics Non-invasive Estimation of Aortic Blood Pressures: A Close Look at Current Devices and Methods
Current Pharmaceutical Design Driving Cellular Plasticity and Survival Through the Signal Transduction Pathways of Metabotropic Glutamate Receptors
Current Neurovascular Research Interactions of Biologically Active Factors and Vascular Mediators During Hypertension in Pregnancy
Current Hypertension Reviews Clinical Pharmacotherapy and Drug Development for Pulmonary Arterial Hypertension
Recent Patents on Cardiovascular Drug Discovery Medicinal and Beneficial Health Applications of Tinospora cordifolia (Guduchi): A Miraculous Herb Countering Various Diseases/Disorders and its Immunomodulatory Effects
Recent Patents on Endocrine, Metabolic & Immune Drug Discovery Pleiotropic Effects of PPARγ Agonist on Hemostatic Activation in Type 2 Diabetes Mellitus
Current Vascular Pharmacology Out-of-Hospital Cardiac Arrest –Optimal Management
Current Cardiology Reviews The Trigeminocardiac Reflex as Oxygen Conserving Reflex in Humans: Its Ischemic Tolerance Potential
Vascular Disease Prevention (Discontinued)