  Agents based on large language models have shown great potential in
accelerating scientific discovery by leveraging their rich background knowledge
and reasoning capabilities. Here, we develop BioDiscoveryAgent, an agent that
designs new experiments, reasons about their outcomes, and efficiently
navigates the hypothesis space to reach desired solutions. We demonstrate our
agent on the problem of designing genetic perturbation experiments, where the
aim is to find a small subset out of many possible genes that, when perturbed,
result in a specific phenotype (e.g., cell growth). Utilizing its biological
knowledge, BioDiscoveryAgent can uniquely design new experiments without the
need to train a machine learning model or explicitly design an acquisition
function. Moreover, BioDiscoveryAgent achieves an average of 18% improvement in
detecting desired phenotypes across five datasets, compared to existing
Bayesian optimization baselines specifically trained for this task. Our
evaluation includes one dataset that is unpublished, ensuring it is not part of
the language model's training data. Additionally, BioDiscoveryAgent predicts
gene combinations to perturb twice as accurately as a random baseline, a task
so far not explored in the context of closed-loop experiment design. The agent
also has access to tools for searching the biomedical literature, executing
code to analyze biological datasets, and prompting another agent to critically
evaluate its predictions. Overall, BioDiscoveryAgent is interpretable at every
stage, representing an accessible new paradigm in the computational design of
biological experiments with the potential to augment scientists' capabilities.
