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Related Concept Videos

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G protein-coupled receptor (GPCR) signaling plays a crucial role in cell functioning. GPCR desensitization is an equally essential process. It allows cells to respond to changing environments and regain sensitivity to new stimuli while preventing unnecessary stimulation when no longer needed. Prolonged exposure to stimuli leads to GPCR desensitization. It involves blocking the receptors from binding and activating additional G proteins. This inhibits activation of downstream effectors, thereby...
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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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Related Experiment Video

Updated: Oct 14, 2025

Monitoring GPCR-β-arrestin1/2 Interactions in Real Time Living Systems to Accelerate Drug Discovery
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Predicting Residence Time of GPCR Ligands with Machine Learning.

Andrew Potterton1,2, Alexander Heifetz2, Andrea Townsend-Nicholson3

  • 1Structural and Molecular Biology, University College London, London, UK.

Methods in Molecular Biology (Clifton, N.J.)
|November 3, 2021
PubMed
Summary

Drug-target residence time, crucial for drug efficacy, can be predicted using machine learning. This study compiles the largest public dataset of GPCR-ligand kinetic data to aid model development.

Keywords:
Binding kineticsDrug discoveryGPCRMachine learningMolecular dynamicsResidence time

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Area of Science:

  • Pharmacology
  • Computational Chemistry
  • Biophysics

Background:

  • Drug-target residence time, the duration a drug binds to its target, is increasingly recognized as critical for therapeutic efficacy, sometimes surpassing binding affinity.
  • Predicting and optimizing residence time is essential for efficient drug discovery, but is hindered by limited available data.
  • G protein-coupled receptors (GPCRs) are a major class of drug targets, making kinetic data for GPCR-ligand interactions particularly valuable.

Purpose of the Study:

  • To establish the largest publicly available repository of GPCR-ligand kinetic data.
  • To summarize experimental evidence on factors influencing drug-target residence time.
  • To outline machine learning workflows for predicting residence time.

Main Methods:

  • Compilation of all currently available ligand kinetic data, focusing on GPCRs.
  • Systematic review of experimental findings on properties influencing residence time.
  • Development and description of two machine learning prediction workflows: a single-target model using ligand features and a multi-target model using molecular dynamics features.

Main Results:

  • Creation of the most extensive public dataset of GPCR-ligand kinetic data to date.
  • Identification of key experimental factors influencing residence time for inclusion in predictive models.
  • Demonstration of two distinct machine learning approaches for residence time prediction.

Conclusions:

  • The curated kinetic dataset and summarized experimental evidence provide a foundation for developing robust residence time prediction models.
  • Machine learning offers a viable strategy for optimizing drug-target residence time in drug discovery.
  • The presented workflows offer practical approaches for computational modeling of drug kinetics.