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Ligand-based machine learning models to classify active compounds for prostaglandin EP2 receptor.

Paul Dupuyds^1,2, 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.

Keywords:

Chemo-informatics Drug discovery EP2 Machine-learning Prostaglandins QSAR

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Published on: September 25, 2021

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.