A team of autonomous GPT-4o-based agents led by a human collaborated to develop experimentally validated nanobodies against SARS-CoV-2. Discover with me how we are entering the era of automated reasoning and work with actionable outputs, and let’s imagine how this will go even further when robotic lab technicians can also carry out the wet lab parts of any research project!
In an era where artificial intelligence continues to reshape how we code, write, and even reason, a new frontier has emerged: AI conducting real scientific research, as several companies (from major established players like Google to dedicated spin offs) are trying to achieve. And we are not talking just about simulations, automated summarization, data crunching, or theoretical outputs, but actually about producing experimentally validated materials, such as biological designs with potential clinical relevance, in this case that I bring you today.
That future just got much closer; very close indeed!
In a groundbreaking paper just published in Nature by researchers from Stanford and the Chan Zuckerberg Biohub, a novel system called the Virtual Lab demonstrated that a human researcher working with a team of large language model (LLM) agents can design new nanobodies—these are tiny, antibody-like proteins that can bind others to block their function—to target fast-mutating variants of SARS-CoV-2. This was not just a narrow chatbot interaction or a tool-assisted paper; it was an open-ended, multi-phase research process led and executed by AI agents, each having a specialized expertise and role, resulting in real-world validated biological molecules that could perfectly move on to downstream studies for actual applicability in disease (in this case Covid-19) treatment.
Let’s delve into it to see how this is serious, replicable research presenting an approach to AI-human (and actually AI-AI) collaborative science works.
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