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Suggestions for the manuscript “The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation”: Below, we outline key strengths of the study as well as areas for improvement, intended to support refinement of the manuscript and broader impact of the work.

Strengths

Innovative Multi-Agent System Design

• Novel orchestration of collaboration between multiple LLM agents with distinct roles (e.g., scientific critic, ML expert), guided by a human-in-the-loop.

• Modular and customizable architecture — tasks can be assigned to different agents or LLMs; tools like ESM, AlphaFold-Multimer, and Rosetta can be swapped.

 Automated and Efficient Workflow

• Pipeline automates team formation, tool selection, role assignment, code generation, and iterative improvement steps.

• Demonstrates low-lift, low-cost screening of trillions of nanobody sequences in minutes, accelerating discovery.

• Faster iteration via parallel meetings (5–10 mins) followed by human adjudication to select optimal outputs.

Biological Validation

• Two promising nanobody candidates targeting new SARS-CoV-2 variants (JN.1 and KP.3) identified and experimentally validated via wet-lab expression and ELISA.

• Strong hybrid validation: AlphaFold-Multimer and Rosetta scores matched prior high-accuracy benchmarks for RBD-antibody binding.

Transparency and Reproducibility

• All steps documented; easy to install and run in Jupyter notebooks.

• GitHub repo well-organized and allows re-running experiments or customizing prompts.

Areas for Improvement

Validation and Evaluation Gaps

• Only one use case (SARS-CoV-2); need additional domains to demonstrate generalizability.

• Results not benchmarked against simpler alternatives (e.g., using ChatGPT alone or running same prompts without agent structure).

• No quantitative assessment of how much each metric (e.g., ESM, Rosetta) contributed to final ranking — weighting system not validated.

Multi-Agent Design Transparency

• Lacks detailed description of how the multi-agent architecture was built or optimized.

• Decisions such as number of parallel meetings (N=3) or agenda structure are not justified; optimization unclear.

• Would benefit from defining how agents are “given” tool access — Appendix suggests tools may have been run externally.

Output Quality and Reliability

• Although agents wrote 99% of words, unclear how many were useful — could be excessive verbosity or hallucinations.

• Human had to correct errors, including basic file I/O issues and tool execution bugs — suggests limited agent autonomy.

• Scientific critic, while helpful, could introduce misleading conclusions without oversight.

Usability and Performance Concerns

• Agents cannot access real-time data (e.g., newly emerging COVID variants).

• Pipeline is not fully automated — requires step-by-step human prompting (e.g., between ESM → AlphaFold → Rosetta stages).

• Variability in output not well quantified; how consistent is the pipeline when re-run?

Presentation and Accessibility

• Figures could use larger text and more interpretation

• Would benefit from a walkthrough video showing pipeline from start to finish.

• Nanobody biology and software tool functions could be better explained for broader audiences.

Scientific Rigor and Detail

• Methods section lacks detail, especially around how LLMs interface with software (e.g., was AlphaFold run internally?).

• Evaluation could be strengthened by adding in vitro neutralization assays or cell-based functional screens beyond ELISA.

• Could compare LLM-designed structures against traditional bioinformatics pipelines to assess true value-added.

Competing interests

The authors declare that they have no competing interests.