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
-
Adverse Effects of Cigarette Smoke and Induction of Oxidative Stress in Cardiomyocytes and Vascular Endothelium
Current Pharmaceutical Design Cardiovascular-Active Venom Toxins: An Overview
Current Medicinal Chemistry The Role of Amino Acids in the Modulation of Cardiac Metabolism During Ischemia and Heart Failure
Current Pharmaceutical Design Cardiovascular Therapeutics Targets on the NO–sGC–cGMP Signaling Pathway: A Critical Overview
Current Drug Targets Endothelial Dysfunction in the Hypertensive State: Mechanisms of Hypertensive Cardiovascular Complications
Current Hypertension Reviews Sex, Stress and their Influence on Respiratory Regulation
Current Pharmaceutical Design Current Issues in Intravenous Fluid Use in Hospitalized Children
Reviews on Recent Clinical Trials Antihypertensive Drugs Metabolism: An Update to Pharmacokinetic Profiles and Computational Approaches
Current Pharmaceutical Design CRH Receptor Signalling: Potential Roles in Pathophysiology
Current Molecular Pharmacology Controlled Release Inhalable Polymeric Microspheres for Treatment of Pulmonary Arterial Hypertension
Current Pharmaceutical Design Review: The Role of MOP and DOP Receptors in Treatment of Diarrheapredominant Irritable Bowel Syndrome
Mini-Reviews in Medicinal Chemistry Lipid-Lowering Therapies for Atherosclerosis: Statins, Fibrates, Ezetimibe and PCSK9 Monoclonal Antibodies
Current Medicinal Chemistry Nitric Oxide: Friendly Rivalry in Tuberculosis
Current Signal Transduction Therapy Hepatic PPARs: Their Role in Liver Physiology, Fibrosis and Treatment
Current Medicinal Chemistry Ventricular and Vascular Stiffening in Aging and Hypertension
Current Hypertension Reviews Urocortins: Putative Role in Cardiovascular Disease
Current Medicinal Chemistry - Cardiovascular & Hematological Agents The Role of NPY and Ghrelin in Anorexia Nervosa
Current Pharmaceutical Design Invertebrate FMRFamide Related Peptides
Protein & Peptide Letters Physiopathological Roles of P2X Receptors in the Central Nervous System
Current Medicinal Chemistry Diagnostic Approach to Mitochondrial Disorders: the Need for a Reliable Biomarker
Current Molecular Medicine