AutoProteinEngine：一种用于蛋白质工程多模态自动机器学习的大语言模型驱动智能体框架
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering
AI总结：
研究针对生物学家缺乏计算知识难以应用深度学习开展蛋白质工程的问题，提出AutoProteinEngine框架，集成LLMs与AutoML，经两项任务验证其性能优于传统方法，降低了深度学习应用门槛。
摘要：
蛋白质工程对生物医学应用至关重要，但传统方法通常效率低下且资源密集。尽管深度学习（DL）模型已展现出应用前景，但对于缺乏专业计算知识的生物学家而言，将其用于蛋白质工程的训练或实施仍具挑战性。为解决这一缺口，我们提出AutoProteinEngine（AutoPE），这是一种利用大语言模型（LLMs）开展蛋白质工程多模态自动机器学习（AutoML）的智能体框架。AutoPE的创新之处在于允许无深度学习背景的生物学家使用自然语言与深度学习模型交互，降低蛋白质工程任务的入门门槛。AutoPE独特地将大语言模型与自动机器学习集成，以处理蛋白质序列和图模态的模型选择、自动超参数优化，以及从蛋白质数据库自动检索数据。我们通过两项真实世界的蛋白质工程任务对AutoPE进行评估，与传统的零样本和手动微调方法相比，其展现出显著的性能提升。通过弥合深度学习与生物学家领域专业知识之间的缺口，AutoPE使研究人员无需广泛的编程知识即可利用深度学习。我们的代码可在此处获取：https://this URL。
英文摘要：
Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive. While deep learning (DL) models have shown promise, their training or implementation into protein engineering remains challenging for biologists without specialized computational expertise. To address this gap, we propose AutoProteinEngine (AutoPE), an agent framework that leverages large language models (LLMs) for multimodal automated machine learning (AutoML) for protein engineering. AutoPE innovatively allows biologists without DL backgrounds to interact with DL models using natural language, lowering the entry barrier for protein engineering tasks. Our AutoPE uniquely integrates LLMs with AutoML to handle model selection for both protein sequence and graph modalities, automatic hyperparameter optimization, and automated data retrieval from protein databases. We evaluated AutoPE through two real-world protein engineering tasks, demonstrating substantial performance improvements compared to traditional zero-shot and manual fine-tuning approaches. By bridging the gap between DL and biologists' domain expertise, AutoPE empowers researchers to leverage DL without extensive programming knowledge. Our code is available at https://github.com/tsynbio/AutoPE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
