Supplementary Information for The Virtual Lab of AI Agents Designs New SARS-CoV-2 Nanobodies

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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AI Agents Design Nanobodies: A Virtual Lab Experiment Against New Covid Variants

This research uses AI agents in a 'virtual lab' to modify existing nanobodies against new SARS-CoV-2 variants (KP.3 and JN.1). The agents employ computational tools like ESM, AlphaFold-Multimer, and Rosetta, combining computational predictions with limited experimental validation to demonstrate improved binding affinity.

Possible Conflicts of Interest

None identified.

Identified Weaknesses

Heavy reliance on computational methods

The study heavily relies on computational predictions and simulations, and although some are validated later, the core discoveries are largely in silico.

Significant human oversight required

While the AI agents showed promise, there's still substantial human intervention in guiding their process and refining outputs. Full automation is not achieved.

Modest improvements upon existing nanobodies

Although the starting nanobodies had some prior efficacy, their effectiveness against the newest variants was limited. The AI improvements might be marginal.

Limited experimental validation

The experimental validation is limited, and the true efficacy of the designed nanobodies against diverse SARS-CoV-2 variants in real-world scenarios remains undetermined.

Rating Explanation

This supplementary information describes a novel approach using AI agents for nanobody design. The combination of computational methods and experimental validation is promising, justifying a rating of 4 despite limitations in the scope of experimental work.

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