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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Opioid receptors, including the mu (μ, MOR), delta (δ, DOR), and kappa (κ, KOR) types, belong to the rhodopsin family of G protein-coupled receptors. These receptors are located throughout the central and peripheral nervous systems and in non-neuronal tissues such as macrophages and astrocytes. Opioid receptor ligands can be categorized into agonists or antagonists. Highly selective agonists include [d-Ala2, MePhe4, Gly(ol)5]-enkephalin or DAMGO for MOR, [D-Pen2,...
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Agonists are drugs that interact with specific receptors in the body to produce a biological response. When an agonist binds to a receptor, it activates or enhances the receptor's function, leading to physiological effects. The interaction between agonist drugs and receptors is crucial for their therapeutic action in various medical treatments.
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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Machine learning models to predict ligand binding affinity for the orexin 1 receptor.

Vanessa Y Zhang1,2,3, Shayna L O'Connor1,2, William J Welsh4

  • 1Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University and Rutgers Biomedical Health Sciences, Piscataway, NJ, USA.

Artificial Intelligence Chemistry
|March 13, 2024
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Summary
This summary is machine-generated.

Researchers developed predictive quantitative structure-activity relationship (QSAR) models for orexin 1 receptor (OX1R) ligands. This approach identified two FDA-approved drugs as potential OX1R ligands, aiding novel drug discovery.

Keywords:
Feature selectionHypocretinHypothalamusMachine learningOrexinQSARRandom forestVirtual screening

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Area of Science:

  • Neuroscience
  • Pharmacology
  • Computational Chemistry

Background:

  • The orexin 1 receptor (OX1R) is a G-protein coupled receptor implicated in various physiological processes.
  • Selective OX1R antagonists show promise for behavioral disorders like drug seeking and overeating.
  • There is a clinical need for novel selective OX1R antagonists due to the lack of approved drugs.

Purpose of the Study:

  • To develop highly predictive quantitative structure-activity relationship (QSAR) models for orexin 1 receptor (OX1R) ligands.
  • To identify novel OX1R ligands, including potential drug candidates, using virtual screening.
  • To establish a robust QSAR modeling framework for future drug discovery efforts targeting OX1R.

Main Methods:

  • A comprehensive dataset of over 1300 OX1R ligands was curated using stringent criteria.
  • Random forest machine learning algorithm with optimized hyperparameters and 12 selected 2D molecular descriptors was employed.
  • Recursive feature elimination with 5-fold cross-validation was used for descriptor selection and model validation.
  • Virtual screening of the DrugBank database was performed using the developed QSAR model.
  • Potential OX1R ligands were confirmed using radiolabeled OX1R binding assays.

Main Results:

  • Highly predictive QSAR models for OX1R ligands were successfully developed.
  • The QSAR model demonstrated high predictivity, validated by external test sets and enrichment studies.
  • Virtual screening identified isavuconazole and cabozantinib, both FDA-approved drugs, as potential OX1R ligands.
  • Binding assays confirmed the interaction of isavuconazole and cabozantinib with the OX1R.

Conclusions:

  • This study presents the first highly predictive QSAR models for a large, diverse dataset of OX1R ligands.
  • The developed QSAR models are valuable tools for the discovery and design of novel OX1R-targeting compounds.
  • The identification of FDA-approved drugs as potential OX1R ligands opens new avenues for therapeutic development.