Abstract
Quantitative Structure - Activity Relationship (QSAR) modeling has been widely used in medicinal chemistry and computational toxicology for many years. Today, as the amount of chemicals is increasing dramatically, QSAR methods have become pivotal for the purpose of handling the data, identifying a decision, and gathering useful information from data processing. The advances in this field have paved a way for numerous alternative approaches that require deep mathematics in order to enhance the learning capability of QSAR models. One of these directions is the use of Multiple Classifier Systems (MCSs) that potentially provide a means to exploit the advantages of manifold learning through decomposition frameworks, while improving generalization and predictive performance. In this paper, we presented MCS as a next generation of QSAR modeling techniques and discuss the chance to mining the vast number of models already published in the literature. We systematically revisited the theoretical frameworks of MCS as well as current advances in MCS application for QSAR practice. Furthermore, we illustrated our idea by describing ensemble approaches on modeling histone deacetylase (HDACs) inhibitors. We expect that our analysis would contribute to a better understanding about MCS application and its future perspectives for improving the decision making of QSAR models.
Keywords: Histone Deacetylase (HDAC) inhibitors, Quantitative Structure –Activity Relationships (QSAR), Multiple classifier system, Ensemble design, Artificial neural network, Histone deacetylase.
Current Topics in Medicinal Chemistry
Title:Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry
Volume: 17 Issue: 30
Author(s): Hai Pham-The, Nguyen-Hai Nam, Doan-Viet Nga, Dang Thanh Hai, Karel Dieguez-Santana, Yovani Marrero-Ponce, Juan A. Castillo-Garit, Gerardo M. Casanola-Martin*Huong Le-Thi-Thu*
Affiliation:
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON,Canada
- School of Medicine and Pharmacy, Vietnam National University (VNU), 144 Xuan Thuy, Hanoi,Vietnam
Keywords: Histone Deacetylase (HDAC) inhibitors, Quantitative Structure –Activity Relationships (QSAR), Multiple classifier system, Ensemble design, Artificial neural network, Histone deacetylase.
Abstract: Quantitative Structure - Activity Relationship (QSAR) modeling has been widely used in medicinal chemistry and computational toxicology for many years. Today, as the amount of chemicals is increasing dramatically, QSAR methods have become pivotal for the purpose of handling the data, identifying a decision, and gathering useful information from data processing. The advances in this field have paved a way for numerous alternative approaches that require deep mathematics in order to enhance the learning capability of QSAR models. One of these directions is the use of Multiple Classifier Systems (MCSs) that potentially provide a means to exploit the advantages of manifold learning through decomposition frameworks, while improving generalization and predictive performance. In this paper, we presented MCS as a next generation of QSAR modeling techniques and discuss the chance to mining the vast number of models already published in the literature. We systematically revisited the theoretical frameworks of MCS as well as current advances in MCS application for QSAR practice. Furthermore, we illustrated our idea by describing ensemble approaches on modeling histone deacetylase (HDACs) inhibitors. We expect that our analysis would contribute to a better understanding about MCS application and its future perspectives for improving the decision making of QSAR models.
Export Options
About this article
Cite this article as:
Pham-The Hai , Nam Nguyen-Hai , Nga Doan-Viet, Hai Thanh Dang , Dieguez-Santana Karel, Marrero-Ponce Yovani , Castillo-Garit A. Juan, Casanola-Martin M. Gerardo*, Le-Thi-Thu Huong *, Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry, Current Topics in Medicinal Chemistry 2017; 17 (30) . https://dx.doi.org/10.2174/1568026618666171212111018
DOI https://dx.doi.org/10.2174/1568026618666171212111018 |
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
-
Vitamin D as a Potential Therapeutic Option in Cancer Treatment: Is There a Role for Chemoprevention?
Anti-Cancer Agents in Medicinal Chemistry Electrochemical Study of DNA Damaged by Oxidation Stress
Combinatorial Chemistry & High Throughput Screening Novel Thiourea Derivatives Bearing Sulfonamide Moiety as Anticancer Agents Through COX-2 Inhibition
Anti-Cancer Agents in Medicinal Chemistry Ultrasound-Based Multimodal Molecular Imaging and Functional Ultrasound Contrast Agents
Current Pharmaceutical Design Metal-Protein Attenuating Compounds (MPACs) for the Treatment of Alzheimers Disease
Drug Design Reviews - Online (Discontinued) Molecular Evidence of Compound Kushen Injection Against Lung Cancer: A Network Pharmacology-Based Investigation from Western Medicine to Traditional Medicine
Anti-Cancer Agents in Medicinal Chemistry Identification of KEY lncRNAs and mRNAs Associated with Oral Squamous Cell Carcinoma Progression
Current Bioinformatics QSAR as a Tool for the Development of Potent Antiproliferative Agents by Inhibition of Choline Kinase
Current Computer-Aided Drug Design Bioinspired Microchip Nanoporous Interferometric Sensor for Sensing and Biosensing Applications
Micro and Nanosystems Applications and Toxicity of Silver Nanoparticles: A Recent Review
Current Topics in Medicinal Chemistry Advances in Computational Structure-Based Drug Design and Application in Drug Discovery
Current Topics in Medicinal Chemistry An Update on “Selenium Containing Compounds from Poison to Drug Candidates: A Review on the GPx-like Activity”
Current Chemical Biology Sol Gel Method Performed for Biomedical Products Implementation
Mini-Reviews in Medicinal Chemistry Modeling and Simulation Approach to Support Dosing and Study Design Requirements for Treating HIV-Related Neuropsychiatric Disease with the NK1-R Antagonist Aprepitant
Current HIV Research Fatty Acid-Mediated Inhibition of Metal Binding to the Multi-Metal Site on Serum Albumin: Implications for Cardiovascular Disease
Current Topics in Medicinal Chemistry Emerging In Vitro Tools to Evaluate Cytochrome P450 and Transporter-Mediated Drug-Drug Interactions
Current Drug Discovery Technologies Nanoscale Formulations and Diagnostics With Their Recent Trends: A Major Focus of Future Nanotechnology
Current Pharmaceutical Design Tumor Marker Detection by Aptamer-Functionalized Graphene Oxide
Current Organic Chemistry The Role of MRI in Treatment Planning for Rectal Cancer - A Review
Current Cancer Therapy Reviews Divergent Synthesis of Novel Dienylbenzothiazoles and Arylidenedibenzoxazepines and Evaluation of Their Antiproliferative and Cytotoxic Properties
Letters in Organic Chemistry