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Ligand-based machine learning models to classify active compounds for prostaglandin EP2 receptor.
Paul Dupuyds1,2, Anita Rayar1, Vaitea Opuu2
1Medetia Pharmaceuticals, 24 Boulevard du Montparnasse, 75015, Paris, France.
Scientific Reports
|January 21, 2026
Summary
Researchers developed a machine learning model to identify active compounds for the prostaglandin EP2 receptor. This model achieved high accuracy, offering a new tool for drug discovery in areas like glaucoma and cancer.
Area of Science:
- Pharmacology
- Medicinal Chemistry
- Computational Biology
Background:
- Prostaglandin receptors are key targets for treating glaucoma, pulmonary hypertension, and cancer.
- Developing selective ligands for prostaglandin receptors remains a challenge.
Purpose of the Study:
- To create a ligand-based machine learning model for classifying EP2 receptor activity.
- To identify selective compounds for the prostaglandin EP2 receptor.
Main Methods:
- Utilized a dataset of 1,826 chemical descriptors.
- Selected 20 descriptors for training random forest algorithms.
- Validated the model using an independent test set and experimental testing.
Main Results:
- The machine learning model achieved an area under the curve (AUC) score > 0.8.
- The classifier demonstrated 88.9% accuracy in predicting EP2 ligand activity.
- Identified novel, experimentally validated EP2 ligands.
Conclusions:
- The developed machine learning model is effective for classifying EP2 receptor activity.
- This workflow can be adapted for other prostaglandin receptors and targets.
- The study provides a valuable tool for accelerating drug discovery for prostaglandin-related diseases.
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