The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation

作者：Kyle Swanson, Wesley Wu, Nash L. Bulaong, John E. Pak, James Zou · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2024 · DOI：10.1101/2024.11.11.623004 · 被引用次数：54 · 研究领域：COVID-19 diagnosis using AI、Cell Image Analysis Techniques、Molecular Communication and Nanonetworks

Abstract Science frequently benefits from teams of interdisciplinary researchers. However, most scientists don’t have access to experts from multiple fields. Fortunately, large language models (LLMs) have recently shown an impressive ability to aid researchers across diverse domains by answering scientific questions. Here, we expand the capabilities of LLMs for science by introducing the Virtual Lab, an AI-human research collaboration to perform sophisticated, interdisciplinary science research. The Virtual Lab consists of an LLM principal investigator agent guiding a team of LLM agents with different scientific backgrounds (e.g., a chemist agent, a computer scientist agent, a critic agent), with a human researcher providing high-level feedback. We design the Virtual Lab to conduct scientific research through a series of team meetings, where all the agents discuss a scientific agenda, and individual meetings, where an agent accomplishes a specific task. We demonstrate the power of the Virtual Lab by applying it to design nanobody binders to recent variants of SARS-CoV-2, which is a challenging, open-ended research problem that requires reasoning across diverse fields from biology to computer science. The Virtual Lab creates a novel computational nanobody design pipeline that incorporates ESM, AlphaFold-Multimer, and Rosetta and designs 92 new nanobodies. Experimental validation of those designs reveals a range of functional nanobodies with promising binding profiles across SA...