---
title: "Empowering Biomedical Discovery with AI Agents — Gao 2024"
description: "It introduces no new dataset, benchmark or model. 195 references, cited by 27 papers here."
authors: ["Shanghua Gao", "Ada Fang", "Yepeng Huang", "Valentina Giunchiglia", "Ayush Noori", "Jonathan Richard Schwarz", "Yasha Ektefaie", "Jovana Kondic", "Marinka Zitnik"]
year: 2024
venue: "arXiv:2404.02831"
source: "https://arxiv.org/abs/2404.02831"
doi: "10.48550/arxiv.2404.02831"
arxiv: "2404.02831"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/gao-2024-empowering-biomedical-discovery-ai-agents/"
---
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# Empowering Biomedical Discovery with AI Agents

**Authors:** [Shanghua Gao](https://md.research.elamur.ai/free/en/authors/shanghua-gao/), Ada Fang, Yepeng Huang, Valentina Giunchiglia, [Ayush Noori](https://md.research.elamur.ai/free/en/authors/ayush-noori/), Jonathan Richard Schwarz, Yasha Ektefaie, Jovana Kondic and [Marinka Zitnik](https://md.research.elamur.ai/free/en/authors/marinka-zitnik/) · **Year:** 2024 · **Venue:** arXiv:2404.02831 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2404.02831) · [PDF](https://arxiv.org/pdf/2404.02831) · [DOI 10.48550/arxiv.2404.02831](https://doi.org/10.48550/arxiv.2404.02831) · [arXiv 2404.02831](https://arxiv.org/abs/2404.02831) · [OpenAlex](https://openalex.org/W4403925918)

**Citations:** 306 citations (OpenAlex, 2026-10-14) · [27](https://md.research.elamur.ai/free/en/papers/gao-2024-empowering-biomedical-discovery-ai-agents/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This is a perspective paper, not an empirical study. It introduces no new dataset, benchmark or model. The authors ask how the long-held goal of an "AI scientist" could become practical in biomedicine. They argue that foundation models on their own are not enough. Next-token prediction suits generating fluent text, but producing genuinely new hypotheses needs creativity and grounding in scientific knowledge. Their proposal is a compound system of collaborating agents that combines LLMs, specialized ML tools, experimental platforms and human experts. Such agents would break problems into subtasks, plan workflows, critique their own outputs, estimate uncertainty and update their knowledge as experimental results arrive. The paper places these agents at the end of a progression: databases, search engines, task-specific ML, interactive learning, and finally agents. It also outlines levels of agent autonomy with examples from genetics, cell biology and chemical biology. The authors stress safety, human oversight and the scarcity of diverse experimental data. The work matters as a roadmap for building trustworthy autonomous research assistants.

## Key points
- Target applications include:
  - virtual cell simulation;
  - programmable control of phenotypes, such as multi-round CRISPR editing campaigns guided by an agent;
  - design of cellular circuits;
  - development of new therapies.
- The paper contrasts agents with earlier tools:
  - The AlphaFold Protein Structure DB holds over 200 million predicted structures, and FoldSeek searches it.
  - Search engines and databases cannot refine queries through reasoning.
  - AlphaFold is limited to the single task of protein folding.
  - Interactive methods such as GENTRL, a reinforcement-learning approach for compound design, struggle to generalize without retraining.
- Key enabling techniques are chat-optimized LLMs like GPT-4, retrieval-augmented generation (which reduces hallucination), RLHF, and multimodal or generative models.
- The authors describe graded levels of autonomy. At the most advanced level illustrated, an agent independently identifies open problems, for example proposing de novo binders for undruggable targets.
- Main challenges are safety safeguards when agents act on real environments and the risk of researchers over-relying on agents. Another is limited experimental data outside a few domains, mainly structural biology and single-cell science, which creates a need for data-efficient generalization.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Massachusetts Institute of Technology](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) — University, Cambridge, United States · 274 works · 82,013 citations
- [Harvard University](https://md.research.elamur.ai/free/en/universities/harvard-university/) — University, Cambridge, United States · 137 works · 45,850 citations
- [Imperial College London](https://md.research.elamur.ai/free/en/universities/imperial-college-london/) — University, London, United Kingdom · 56 works · 13,671 citations
- [Broad Institute](https://md.research.elamur.ai/free/en/labs/broad-institute/) — Research institute, Cambridge, United States · 5 works · 39,829 citations

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## Cite

Cite as: Shanghua Gao, Ada Fang, Yepeng Huang, Valentina Giunchiglia, Ayush Noori, Jonathan Richard Schwarz, Yasha Ektefaie, Jovana Kondic and Marinka Zitnik. 2024. Empowering Biomedical Discovery with AI Agents. arXiv:2404.02831.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{gao2024empowering,
  title = {{Empowering Biomedical Discovery with AI Agents}},
  author = {Shanghua Gao, Ada Fang, Yepeng Huang, Valentina Giunchiglia, Ayush Noori, Jonathan Richard Schwarz, Yasha Ektefaie, Jovana Kondic and Marinka Zitnik},
  year = {2024},
  doi = {10.48550/arxiv.2404.02831},
  eprint = {2404.02831},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2404.02831}
}
```

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## References

195 references, 69 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 3 November 2026*