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evaluation includes one dataset that is unpublished, ensuring it is not part of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.

BioDiscoveryAgent 是一个基于大型语言模型的代理，通过利用其丰富的背景知识和推理能力，加速科学探索。它在设计基因干预实验中表现出色，能够在不需要训练机器学习模型或显式设计获取函数的情况下，利用其生物学知识独特地设计新的实验。与现有特定训练任务的贝叶斯优化基准相比，BioDiscoveryAgent 在检测所需表型方面平均提高了 18％。它还能准确预测基因组合来干扰实验设计，并具有搜索生物医学文献、执行代码以分析生物数据集，并请另一代理对其预测进行评估的功能。总之，BioDiscoveryAgent 代表了计算生物实验设计中可解释性的全新范例，具备增强科学家能力的潜力。