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G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
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Cells can detect chemical cues in their environment and reorganize the cytoskeleton to migrate toward them or away from them. This directional migration, called chemotaxis, is essential during embryogenesis and development, immune response, tissue repair and regeneration, and reproduction. These chemical cues can either attract or repel the cell's movement. For example, axon development is determined by a combination of chemoattractants and chemorepellents that direct the growing axon...
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Related Experiment Video

Updated: Aug 8, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

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Chemokine Receptors-Structure-Based Virtual Screening Assisted by Machine Learning.

Paulina Dragan1, Matthew Merski1, Szymon Wiśniewski1

  • 1Faculty of Chemistry, University of Warsaw, 02-093 Warsaw, Poland.

Pharmaceutics
|February 25, 2023
PubMed
Summary

Chemokine receptors (CCR1-6) are crucial in inflammatory diseases. This study combined structure-based virtual screening and machine learning to identify novel drug candidates targeting CCR2 and CCR3, showing promise for treating conditions like rheumatoid arthritis.

Keywords:
CCR2CCR3G protein-coupled receptorsGlideLightGBMTensorFlowcheminformaticschemokine receptorsdrug discoverygradient-boosting machinemachine learningmolecular dockingneural networkvirtual screening

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Area of Science:

  • Immunology and Pharmacology
  • Computational Chemistry
  • Drug Discovery

Background:

  • Chemokines regulate immune cell migration and are implicated in inflammatory diseases.
  • Conventional chemokine receptors (CCR1-6) are G protein-coupled receptors linked to multiple sclerosis, asthma, and rheumatoid arthritis.
  • Structure-based drug design requires detailed receptor information.

Purpose of the Study:

  • To evaluate the utility of available chemokine receptor structures for drug design.
  • To identify novel active compounds for CCR2 and CCR3 using structure-based virtual screening.
  • To develop and compare machine learning models for ligand-based drug design of CCR inhibitors.

Main Methods:

  • Collected crystal, cryo-EM, and homology models for CCR1-6.
  • Performed structure-based virtual screening targeting CCR2 and CCR3.
  • Utilized known CCR inhibitors from ChEMBL as training data for machine learning models (LightGBM and Keras/TensorFlow NN).
  • Assessed the predictive performance of virtual screening and machine learning methods for CCR2/CCR3 activity and selectivity.

Main Results:

  • Structure-based virtual screening identified several novel active compounds for CCR2 and CCR3.
  • Machine learning models were trained using known CCR inhibitors.
  • A combination of methods successfully proposed active ligands for CCR2 and CCR3.
  • Two compounds were consistently predicted as CCR3 active by Glide, Keras/TensorFlow NN, and LightGBM.
  • The study assessed the subtype selectivity prediction capabilities of the tested methods.

Conclusions:

  • Available chemokine receptor structures are valuable for structure-based drug design.
  • Integrated structure-based virtual screening and machine learning approaches can effectively identify potent chemokine receptor modulators.
  • This study provides promising lead compounds for CCR2 and CCR3 and advances computational methods for drug discovery in inflammatory diseases.