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High-Content Screening Assay for the Identification of Antibody-Dependent Cellular Cytotoxicity Modifying Compounds

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AI-Enforced Ultra-Large Virtual Screening Discovers Potent CD28 Binders.

Saurabh Upadhyaya, Michele Roggia, Shaoren Yuan

Biorxiv : the Preprint Server for Biology

|April 3, 2026

Summary

This summary is machine-generated.

This study introduces PyRMD2Dock, an AI-driven platform for structure-based virtual screening (SBVS). It successfully identified novel small molecules targeting the immune receptor CD28, demonstrating a powerful new approach for drug discovery.

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Area of Science:

Computational chemistry
Immunology
Drug discovery

Background:

Targeting protein-protein interactions (PPIs) with small molecules is difficult due to their challenging binding sites.
Traditional structure-based virtual screening (SBVS) faces computational limitations with large chemical libraries.

Purpose of the Study:

To demonstrate the first real-world application of the AI-powered PyRMD2Dock platform for structure-based virtual screening.
To discover novel small-molecule modulators of the immune receptor CD28.

Main Methods:

Utilized PyRMD2Dock, an AI-enhanced SBVS workflow, to screen 2.4 million compounds against CD28.
Trained machine learning models on initial docking data to rapidly screen an additional ~46 million compounds.
Prioritized ligands based on interaction filtering and clustering.

Main Results:

Identified multiple direct CD28 binders from the screened chemical library.
Developed lead compounds (100 and 104) with submicromolar affinity (K_d = 343.8 nM and 407.1 nM).
Demonstrated potent disruption of CD28-CD80 interaction and functional blockade in cellular assays, reducing cytokine secretion.

Conclusions:

PyRMD2Dock is a highly scalable and effective platform for identifying small-molecule modulators of challenging targets like immune receptors.
This AI-driven approach overcomes computational bottlenecks in mining massive chemical libraries for drug discovery.