Abstract
Background: Tuberculosis is the second leading cause of death from an infectious disease worldwide after HIV, thus reasoning the expeditions in antituberculosis research. The rising number of cases of infection by resistant forms of M. tuberculosis has given impetus to the development of novel drugs that have different targets and mechanisms of action against the bacterium. Methods: In this study, we have used machine learning algorithms on the available high throughput screening data of inhibitors of fructose bisphosphate aldolase, an enzyme central to the glycolysis pathway in M. tuberculosis, to build predictive classification models to identify actives against Mycobacterium tuberculosis, the causative organism of tuberculosis. We used Naïve Bayes, Random Forest and C4.5 J48 algorithms available from Weka were used for building predictive classification models. Additionally, a set of most relevant attributes was selected using genetic search algorithm which offered improved model performance by avoiding over fitting and generating faster and cost effective models. Results: The model built using machine learning methods in this study provided good accuracy of classification of test compounds which suggests that in silico methods can be successfully used for screening of large datasets to identify potential drug leads. The substructure fragment analysis serves to further potentiate the M. tuberculosis drug development process as it would facilitate identification of structural fragments that are responsible for biological activity against this crucial glycolysis pathway target.
Keywords: Tuberculosis, fructose bisphosphate aldolase, cheminformatics, machine learning, substructure, glycolysis pathway.
Combinatorial Chemistry & High Throughput Screening
Title:Cheminformatics Based Machine Learning Approaches for Assessing Glycolytic Pathway Antagonists of Mycobacterium tuberculosis
Volume: 19 Issue: 8
Author(s): Kanupriya Tiwari, Salma Jamal, Sonam Grover, Sukriti Goyal, Aditi Singh and Abhinav Grover
Affiliation:
Keywords: Tuberculosis, fructose bisphosphate aldolase, cheminformatics, machine learning, substructure, glycolysis pathway.
Abstract: Background: Tuberculosis is the second leading cause of death from an infectious disease worldwide after HIV, thus reasoning the expeditions in antituberculosis research. The rising number of cases of infection by resistant forms of M. tuberculosis has given impetus to the development of novel drugs that have different targets and mechanisms of action against the bacterium. Methods: In this study, we have used machine learning algorithms on the available high throughput screening data of inhibitors of fructose bisphosphate aldolase, an enzyme central to the glycolysis pathway in M. tuberculosis, to build predictive classification models to identify actives against Mycobacterium tuberculosis, the causative organism of tuberculosis. We used Naïve Bayes, Random Forest and C4.5 J48 algorithms available from Weka were used for building predictive classification models. Additionally, a set of most relevant attributes was selected using genetic search algorithm which offered improved model performance by avoiding over fitting and generating faster and cost effective models. Results: The model built using machine learning methods in this study provided good accuracy of classification of test compounds which suggests that in silico methods can be successfully used for screening of large datasets to identify potential drug leads. The substructure fragment analysis serves to further potentiate the M. tuberculosis drug development process as it would facilitate identification of structural fragments that are responsible for biological activity against this crucial glycolysis pathway target.
Export Options
About this article
Cite this article as:
Tiwari Kanupriya, Jamal Salma, Grover Sonam, Goyal Sukriti, Singh Aditi and Grover Abhinav, Cheminformatics Based Machine Learning Approaches for Assessing Glycolytic Pathway Antagonists of Mycobacterium tuberculosis, Combinatorial Chemistry & High Throughput Screening 2016; 19 (8) . https://dx.doi.org/10.2174/1386207319666160610080716
DOI https://dx.doi.org/10.2174/1386207319666160610080716 |
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
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
Integrating Network Pharmacology and Traditional Medicine: A New Perspective in Drug Mechanism Research
Network pharmacology is a network construction and network topology analysis strategy that combines pharmacology and pharmacodynamics. In recent years, network pharmacology has emerged as a powerful tool that can be integrated with pharmacology. Natural products commonly function in multicomponent, multitarget, and multipathway systems. Some examples encompass Ayurveda, traditional Chinese medicines ...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
-
Death Due to COVID-19 in an Infant with Combined Immunodeficiencies
Endocrine, Metabolic & Immune Disorders - Drug Targets Pyrazolines: A Biological Review
Mini-Reviews in Medicinal Chemistry Fight Against H1N1 Influenza A Virus: Recent Insights Towards the Development of Druggable Compounds
Current Medicinal Chemistry Development & Pharmaceutical Characterization of Isoniazid Loaded Solid Lipid Nanoparticle Drug Delivery Approach
Current Drug Therapy Design, Synthesis and Biological Evaluation of Novel Tetrahydroquinoline Based Propanehydrazides as Antitubercular Agents
Letters in Drug Design & Discovery Resistant Tuberculosis: the Latest Advancements of Second-line Antibiotic Inhalation Products
Current Pharmaceutical Design 4-Hydroxy-2-pyridone Derivatives and the δ-pyrone Isostere as Novel Agents Against Mycobacterium smegmatis Biofilm Inhibitors
Medicinal Chemistry Investigating Human P450s Involved in Drug Metabolism via Homology with High-Resolution P450 Crystal Structures of the CYP2C Subfamily
Current Drug Metabolism Inhalation Delivery of Protein Therapeutics
Inflammation & Allergy - Drug Targets (Discontinued) DESIGN, <i>In silico</i> Modeling, Toxicity study and Synthesis of Novel Substituted Semicarbazide Derivatives of Pyrimidine: An Antitubercular Agent
Current Bioactive Compounds Immunological Aspects of Adult T-Cell Leukemia/Lymphoma (ATLL), a Possible Neoplasm of Regulatory T-Cells
Current Immunology Reviews (Discontinued) Specifically Targeting Mtb Cell-Wall and TMM Transporter: The Development of MmpL3 Inhibitors
Current Protein & Peptide Science Stroke and COVID-19 Pandemic: The Dilemma
Coronaviruses Recent Advances on the Synthesis of Heterocycles from Diazo Compounds
Current Organic Chemistry Protein Microarrays for Studies of Drug Mechanisms and Biomarker Discovery in the Era of Systems Biology
Current Pharmaceutical Design New Perspectives in Drug Delivery Systems for the Treatment of Tuberculosis
Current Medicinal Chemistry Molecular Biological Roles of Ursolic Acid in the Treatment of Human Diseases
Current Bioactive Compounds New Developments in Antimicrobial Use in Sepsis
Current Pharmaceutical Design The α/β Hydrolase Fold Proteins of Mycobacterium tuberculosis, with Reference to their Contribution to Virulence
Current Protein & Peptide Science Therapeutic Applications of Nanoemulsion Based Drug Delivery Systems: A Review of Patents in Last Two Decades
Recent Patents on Drug Delivery & Formulation