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Forward genetic screens
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GPCRVS - AI-driven Decision Support System for GPCR Virtual Screening.

Dorota Latek1, Khushil Prajapati1, Paulina Dragan1

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

International Journal of Molecular Sciences
|March 13, 2025
PubMed
Summary

GPCRVS is a new machine learning system that predicts drug activity for G protein-coupled receptors (GPCRs). It assesses compound activity, pharmacological effects, and binding modes, aiding in the design of targeted therapeutics.

Keywords:
G protein-coupled receptorsdecision support systemsdeep learningdrug selectivitygradient boosting machinesmachine learningmolecular dockingneural networksvirtual screening

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Area of Science:

  • Pharmacology
  • Computational Chemistry
  • Bioinformatics

Background:

  • G protein-coupled receptors (GPCRs) are the largest class of drug targets due to their accessibility and well-defined signaling.
  • GPCRs' sensitivity to chemical structure enables precise drug design.
  • Recent advancements in structural biology provide sufficient data for machine learning applications in GPCR drug discovery.

Purpose of the Study:

  • To introduce GPCRVS, an efficient machine learning system for predicting compound activity against GPCR targets.
  • To enable online assessment of drug candidates, including challenging peptide- and protein-binding GPCRs.
  • To support decision-making in drug design by evaluating activity range, pharmacological effects, and binding modes.

Main Methods:

  • Development of GPCRVS, a machine learning system utilizing multiclass classification.
  • Handling of incomplete and fuzzy biological data for robust predictions.
  • Validation using ChEMBL and Google Patents datasets for Class B GPCRs and chemokine receptors.

Main Results:

  • GPCRVS efficiently assesses compound activity, pharmacological effects, and potential binding modes for various GPCRs.
  • The system handles diverse compound types, from small molecules to short peptides.
  • Predictions are benchmarked against known active ligands for accuracy.

Conclusions:

  • GPCRVS offers a powerful tool for virtual screening and drug design targeting GPCRs.
  • The system's ability to handle complex GPCRs and diverse data enhances its utility.
  • GPCRVS facilitates the development of selective and specific drugs by predicting compound behavior.