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Pharmacologic Induction of Epidermal Melanin and Protection Against Sunburn in a Humanized Mouse Model
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QSAR study on melanocortin-4 receptors by support vector machine.

Eslam Pourbasheer1, Siavash Riahi, Mohammad Reza Ganjali

  • 1Faculty of Chemistry, University of Tehran, Center of Excellence in Electrochemistry, Tehran, Iran.

European Journal of Medicinal Chemistry
|December 25, 2009
PubMed
Summary

Quantitative structure-activity relationship (QSAR) models were developed to predict melanocortin-4 receptor (MC4R) binding affinities. A support vector machine (SVM) model demonstrated superior predictive performance for piperazinecyclohexane derivatives.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Melanocortin-4 receptor (MC4R) plays a crucial role in regulating energy balance and appetite.
  • Developing selective MC4R ligands is a key therapeutic strategy for obesity and related metabolic disorders.
  • Understanding the structure-activity relationships of MC4R ligands is essential for rational drug design.

Purpose of the Study:

  • To establish quantitative structure-activity relationship (QSAR) models for predicting the binding affinities (Ki) of trans-4-(4-chlorophenyl) pyrrolidine-3-carboxamides of piperazinecyclohexanes to the MC4R.
  • To identify key molecular descriptors that influence MC4R binding affinity.
  • To compare the performance of linear (MLR) and nonlinear (SVM) QSAR models.

Main Methods:

  • Calculation of molecular descriptors for a series of piperazinecyclohexane derivatives.
  • Utilizing a genetic algorithm (GA) for optimal descriptor selection.
  • Construction of QSAR models using multiple linear regression (MLR) and support vector machine (SVM).
  • Rigorous model validation using Leave-One-Out (LOO), Leave-Group-Out (LGO) cross-validation, external test sets, and chance correlation analysis.

Main Results:

  • The support vector machine (SVM) model exhibited superior predictive ability compared to the multiple linear regression (MLR) model.
  • The developed SVM model achieved high statistical significance with R(2)(train)=0.908, Q(2)(LOO)=0.781, and Q(2)(LGO)=0.872.
  • Key molecular descriptors influencing MC4R binding affinity were identified through the GA-MLR and GA-SVM approaches.

Conclusions:

  • The developed nonlinear QSAR model using SVM provides a reliable tool for predicting MC4R binding affinities.
  • The findings facilitate the design of novel piperazinecyclohexane derivatives with enhanced MC4R binding profiles.
  • This study contributes to the understanding of molecular determinants governing MC4R-ligand interactions, aiding in the development of MC4R-targeted therapeutics.