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Related Experiment Video

Updated: May 28, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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An Explainable 2D-QSAR Machine Learning Approach for Predicting COX-2 Inhibitory Activity Using Molecular

Mebarka Ouassaf1, Bader Y Alhatlani2

  • 1Group of Computational and Medicinal Chemistry, LMCE Laboratory, University of Biskra, Biskra 07000, Algeria.

Pharmaceuticals (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

This study developed an explainable machine learning model to predict cyclooxygenase-2 (COX-2) inhibition, accelerating the discovery of novel anti-inflammatory drugs through reliable virtual screening.

Keywords:
COX-2 inhibitorsMorgan fingerprintsQSARRandom ForestSHAPcancerdrug discoveryexplainable artificial intelligencemachine learningvirtual screening

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Area of Science:

  • Computational chemistry and cheminformatics
  • Pharmacology and drug discovery
  • Artificial intelligence in medicine

Background:

  • Cyclooxygenase-2 (COX-2) is a key target for anti-inflammatory drug development.
  • Identifying novel COX-2 inhibitors is crucial for pharmaceutical research.
  • Existing methods require enhancement for efficient virtual screening.

Purpose of the Study:

  • To develop a robust and interpretable machine learning (ML) framework for predicting COX-2 inhibitory activity.
  • To support and accelerate virtual screening efforts in drug discovery.
  • To identify key structural features contributing to COX-2 inhibition.

Main Methods:

  • A dataset of 2052 compounds from the ChEMBL database was utilized.
  • Molecular structures were represented using Morgan fingerprints from SMILES.
  • Ensemble ML methods, including Random Forest, were trained and validated.
  • SHAP analysis was employed for model interpretability.

Main Results:

  • Ensemble methods, particularly Random Forest, showed superior predictive performance.
  • Y-randomization tests confirmed the robustness of the model.
  • SHAP analysis identified pharmacophore-aligned structural features driving activity.
  • A web application for real-time COX-2 activity prediction was developed.

Conclusions:

  • Explainable ML effectively predicts COX-2 inhibitory activity.
  • The developed framework enhances COX-2 inhibitor discovery.
  • The tool facilitates efficient virtual screening in drug development.