Abstract
A metabolic pathway is a series of biological processes providing necessary molecules and energies for an organism, which could be essential to the lives of the living organisms. Most metabolic pathways require the involvement of compounds and given a compound it is helpful to know what types of metabolic pathways the compound participates in. In this study, compounds are first represented by molecular fragments which are then delivered to a prediction engine called Sequential Minimal Optimization (SMO) for predictions. Maximum relevance and minimum redundancy (mRMR) and incremental feature selection are adopted to extract key features based on which an optimal prediction engine is established. The proposed method is effective comparing to the random forest, Dagging and a popular method that integrating chemical-chemical interactions and chemical-chemical similarities. We also make predictions using some compounds with unknown metabolic pathways and choose 17 compounds for analysis. The results indicate that the method proposed may become a useful tool in predicting and analyzing metabolic pathways.
Keywords: Compound, metabolic pathway, molecular fragment, minimum redundancy maximum relevance, incremental feature selection, sequential minimal optimization.
Combinatorial Chemistry & High Throughput Screening
Title:Predicting the types of metabolic pathway of compounds using molecular fragments and sequential minimal optimization
Volume: 19 Issue: 2
Author(s): Lei Chen, Chen Chu and Kaiyan Feng
Affiliation:
Keywords: Compound, metabolic pathway, molecular fragment, minimum redundancy maximum relevance, incremental feature selection, sequential minimal optimization.
Abstract: A metabolic pathway is a series of biological processes providing necessary molecules and energies for an organism, which could be essential to the lives of the living organisms. Most metabolic pathways require the involvement of compounds and given a compound it is helpful to know what types of metabolic pathways the compound participates in. In this study, compounds are first represented by molecular fragments which are then delivered to a prediction engine called Sequential Minimal Optimization (SMO) for predictions. Maximum relevance and minimum redundancy (mRMR) and incremental feature selection are adopted to extract key features based on which an optimal prediction engine is established. The proposed method is effective comparing to the random forest, Dagging and a popular method that integrating chemical-chemical interactions and chemical-chemical similarities. We also make predictions using some compounds with unknown metabolic pathways and choose 17 compounds for analysis. The results indicate that the method proposed may become a useful tool in predicting and analyzing metabolic pathways.
Export Options
About this article
Cite this article as:
Chen Lei, Chu Chen and Feng Kaiyan, Predicting the types of metabolic pathway of compounds using molecular fragments and sequential minimal optimization, Combinatorial Chemistry & High Throughput Screening 2016; 19 (2) . https://dx.doi.org/10.2174/1386207319666151110122453
DOI https://dx.doi.org/10.2174/1386207319666151110122453 |
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
-
Review of Recent Clinical Developments and Patents for the Treatment of Autoimmune and Inflammatory Diseases by Mesenchymal Stromal Cells
Recent Patents on Regenerative Medicine Emerging Prospects for the Study of Colorectal Cancer Stem Cells using Patient-derived Organoids
Current Cancer Drug Targets Identification of a Novel Human Peroxisomal 2,4-Dienoyl-CoA Reductase Related Protein Using the M13 Phage Protein VI Phage Display Technology
Combinatorial Chemistry & High Throughput Screening Bcl-2 Targeted-Therapy for the Treatment of Head and Neck Squamous Cell Carcinoma
Recent Patents on Anti-Cancer Drug Discovery Functional Expression of Human Methionine Aminopeptidase Type 1 in Saccharomyces Cerevisiae
Protein & Peptide Letters The Application of Modified SBA-15 as a Chemosensor
Current Nanomaterials Study of drug-drug combinations based on molecular descriptors and physicochemical properties
Combinatorial Chemistry & High Throughput Screening Synthesis, Selective Cancer Cytotoxicity and Mechanistic Studies of Novel Analogs of Lantadenes
Anti-Cancer Agents in Medicinal Chemistry Electron Paramagnetic Resonance (EPR) Spectroscopy: A Versatile and Powerful Tool in Pharmaceutical and Biomedical Analysis
Current Pharmaceutical Analysis AFM-Based Single Molecule Techniques: Unraveling the Amyloid Pathogenic Species
Current Pharmaceutical Design A Novel Task Specific & Thermally Stable Ionic Liquid (TBA Acetate) for the Synthesis of Pyran Annulated Heterocyclic System
Letters in Organic Chemistry Human Hepatocytes in Primary Culture: The Choice to Investigate Drug Metabolism in Man
Current Drug Metabolism Aptamers Against Cell Surface Receptors: Selection, Modification and Application
Current Medicinal Chemistry Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential
Current Topics in Medicinal Chemistry Humic Acids as Therapeutic Compounds in Lead Intoxication
Current Clinical Pharmacology The Metabolite Trimethylamine-N-Oxide is an Emergent Biomarker of Human Health
Current Medicinal Chemistry A Review on One-Dimensional Ternary Germanate Nanomaterials
Recent Patents on Nanotechnology Assessment of Antioxidant Capacity of Natural Products
Current Pharmaceutical Biotechnology Use of Topiceuticals (Topically Applied, Peripherally Acting Drugs) in the Treatment of Chronic Pain
Current Drug Therapy Carbon Quantum Dots: Surface Passivation and Functionalization
Current Organic Chemistry