Abstract
Despite being identified as the most potent receptor related to vasoconstriction, human urotensin-II receptor (hUT) has not been fully explored as a target for the treatment of cardiovascular diseases. In view of this and with an aim to identify precise structural requirements for binding of hUT antagonists, we endeavoured to develop, for the first time, multivariate QSAR models using chemometric methods like partial least squares (PLS) and feed-forward neural network (FFNN). A set of 48 pyrrolidine derivatives having hUT binding affinity was used for multivariate model development. The accuracy and predictability of the developed models was evaluated using crossvalidation. The PLS model showed good correlation between selected descriptors and Ki values (r2 =0.745 and r2 (CV) =0.773). However, the predictive performance of FFNN was better than the PLS technique with r2 =0.810. The study clearly suggests the role of lipophilic and steric descriptors in the ligand-hUT interactions. The QSAR models generated can be successfully extended to predict the binding affinities and for the effective design of novel hUT antagonists.
Keywords: QSAR, urotensin-II, PLS, FFNN, descriptors, TSAR.
Current Computer-Aided Drug Design
Title:Use of Diverse Chemometric and Validation Methods to Accurately Predict Human Urotensin-II Receptor Antagonist Activity
Volume: 11 Issue: 4
Author(s): Anubhuti Pandey, Sarvesh Paliwal, Rakesh Yadav and Shailendra Paliwal
Affiliation:
Keywords: QSAR, urotensin-II, PLS, FFNN, descriptors, TSAR.
Abstract: Despite being identified as the most potent receptor related to vasoconstriction, human urotensin-II receptor (hUT) has not been fully explored as a target for the treatment of cardiovascular diseases. In view of this and with an aim to identify precise structural requirements for binding of hUT antagonists, we endeavoured to develop, for the first time, multivariate QSAR models using chemometric methods like partial least squares (PLS) and feed-forward neural network (FFNN). A set of 48 pyrrolidine derivatives having hUT binding affinity was used for multivariate model development. The accuracy and predictability of the developed models was evaluated using crossvalidation. The PLS model showed good correlation between selected descriptors and Ki values (r2 =0.745 and r2 (CV) =0.773). However, the predictive performance of FFNN was better than the PLS technique with r2 =0.810. The study clearly suggests the role of lipophilic and steric descriptors in the ligand-hUT interactions. The QSAR models generated can be successfully extended to predict the binding affinities and for the effective design of novel hUT antagonists.
Export Options
About this article
Cite this article as:
Pandey Anubhuti, Paliwal Sarvesh, Yadav Rakesh and Paliwal Shailendra, Use of Diverse Chemometric and Validation Methods to Accurately Predict Human Urotensin-II Receptor Antagonist Activity, Current Computer-Aided Drug Design 2015; 11 (4) . https://dx.doi.org/10.2174/1874609809666151223093650
DOI https://dx.doi.org/10.2174/1874609809666151223093650 |
Print ISSN 1573-4099 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6697 |
- 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
-
Lithium and Kidney, 60 Years Later
Current Drug Safety An Insight into the Recent Diabetes Trials: What is the Best Approach to Prevent Macrovascular and Microvascular Complications?
Current Diabetes Reviews Density Functional Theory, Docking, Bioisosteric Replacement, Pharmacophore Perception, Physical Chemical Analyses of the Interactions of Novel PIM-1 Inhibitors with Suitable Pharmacokinetic Properties for Cancer Treatment
Current Physical Chemistry Building a Bridge Between Clinical and Basic Research: The Phenotypic Elements of Familial Predisposition to Type 1 Diabetes
Current Medicinal Chemistry Relaxin, Insulin and Diabetes: An Intriguing Connection
Current Diabetes Reviews Raxofelast, (±)5-(Acetyloxy)-2,3-dihydro-4,6,7-trimethyl-2-benzofuranacetic Acid: A New Antioxidant to Modulate the Inflammatory Response During Ischemia-Reperfusion Injury and Impaired Wound Healing
Mini-Reviews in Medicinal Chemistry Microalbuminuria and the Hypertensive Disorders of Pregnancy
Current Hypertension Reviews Podocyte Mitosis – A Catastrophe
Current Molecular Medicine Diabetes and Complications: Cellular Signaling Pathways, Current Understanding and Targeted Therapies
Current Drug Targets Plant Compounds for the Treatment of Diabetes, a Metabolic Disorder: NF-κB as a Therapeutic Target
Current Pharmaceutical Design Therapeutic Management Strategies for Type 2 Diabetes
Current Diabetes Reviews Calcifediol – More than the Stepchild of CKD-MBD Therapy?
Current Vascular Pharmacology Kinin Receptors in Vascular Biology and Pathology
Current Vascular Pharmacology Patent Selections:
Recent Patents on Endocrine, Metabolic & Immune Drug Discovery Pleiotropic Effects of Drugs Inhibiting the Renin-Angiotensin-Aldosterone System
Current Pharmaceutical Design Dipeptidyl-peptidase 4 Inhibition: Linking Metabolic Control to Cardiovascular Protection
Current Pharmaceutical Design Sulfonyl Group-Containing Compounds in the Design of Potential Drugs for the Treatment of Diabetes and Its Complications
Current Medicinal Chemistry Polycystic Ovary Syndrome as a Proinflammatory State: The Role of Adipokines
Current Pharmaceutical Design Increased Risk of Falls, Fall-related Injuries and Fractures in People with Type 1 and Type 2 Diabetes - A Nationwide Cohort Study
Current Drug Safety ROS Acts as a Double-Edged Sword in the Pathogenesis of Type 2 Diabetes Mellitus: Is Nrf2 a Potential Target for the Treatment?
Mini-Reviews in Medicinal Chemistry