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A Kinetic Fluorescence-based Ca2+ Mobilization Assay to Identify G Protein-coupled Receptor Agonists, Antagonists, and Allosteric Modulators
Published on: February 20, 2018
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A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists
Jooseong Oh1, Hyi-Thaek Ceong2, Dokyun Na3
1School of Biological Sciences and Technology, Chonnam National University, Gwangju, 61186, Republic of Korea.
BMC Bioinformatics
|August 18, 2022
Summary
A new machine learning model accurately identifies G-protein coupled receptor (GPCR) agonists and antagonists. This tool aids in drug discovery by efficiently screening potential GPCR-binding compounds.
Area of Science:
- Pharmacology and Cheminformatics
- Computational Drug Discovery
Background:
- G-protein coupled receptors (GPCRs) are crucial cell signaling proteins involved in numerous diseases.
- Dysfunctional GPCRs are implicated in conditions like cancer and diabetes.
- Targeting GPCRs is a key strategy in drug development, with many marketed drugs acting on these receptors.
Purpose of the Study:
- To develop a machine learning model for identifying GPCR ligands.
- To classify these ligands as either agonists or antagonists.
Main Methods:
- A two-step prediction model was designed.
- Feature selection identified 990 relevant features from 5270 molecular descriptors.
- The model was trained on 4590 GPCR ligands from drug databases.
Main Results:
- The model achieved an Area Under the ROC Curve (AUC) of 0.795.
- Sensitivity, specificity, and accuracy were 0.716, 0.744, and 0.733, respectively.
- 70% of FDA-approved GPCR-targeting drugs were correctly classified.
Conclusions:
- Ligand-GPCR interaction prediction is vital for understanding drug mechanisms.
- The developed model facilitates efficient virtual screening of GPCR agonists and antagonists.
- This tool can significantly aid pharmaceutical sciences in drug discovery efforts.
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