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Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy

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Rise of AI Technologies in Virtual Screening.

Marco Cecchini¹, Hryhory Sinenka^1,2

¹Institut de Chimie de Strasbourg, UMR7177, CNRS, Université de Strasbourg, 67081 Strasbourg Cedex, France.

Journal of Chemical Information and Modeling

|April 16, 2026

Summary

This summary is machine-generated.

AI models like Boltz-2 are improving predictions for protein-ligand interactions. Boltz-2 demonstrated superior classification accuracy in virtual screening, outperforming other methods.

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Area of Science:

Computational chemistry
Artificial intelligence in drug discovery

Background:

Emergence of AI foundational models for predicting protein-ligand interactions and binding affinities.
Need for robust methods to analyze large virtual screening datasets.

Purpose of the Study:

To evaluate the performance of the Boltz-2 AI model on a challenging dataset for protein-ligand interaction prediction.
To compare Boltz-2 against other rescoring strategies in virtual screening.

Main Methods:

Utilized a dataset comprising ten ultralarge virtual screening hit lists.
Employed in vitro binding assays for target validation.
Challenged the Boltz-2 model on this difficult dataset.

Main Results:

Boltz-2 emerged as the top-performing classifier.
Achieved a success rate twice that of any other rescoring strategy.
Demonstrated straightforward, accurate, efficient, and robust ligand classifications.

Conclusions:

Boltz-2 offers a significant advancement in predicting protein-ligand interactions.
Enables accurate, large-scale compound rankings on standard computational resources.
Paves the way for more efficient drug discovery pipelines.