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Google DeepMind·· 2026-05-16AI 评分36

用 Co-Scientist 寻找新型传染病背后的分子开关

Finding the molecular switches behind new infectious diseases

AI 导读

剑桥大学 Clare Bryant 团队用 Google DeepMind 的 Co-Scientist,寻找病原体跨物种传播引发脓毒症等重症的分子开关。该工具生成并排序假设,优先锁定一个她此前未关注的蛋白,并逐步收窄到具体氨基酸。原本需两三年实验才能定位精确氨基酸,团队如今有望六个月内完成。

正文

The majority of emerging infectious diseases are caused by pathogens that jump from animals to humans, such as Ebola, HIV, flu and Covid-19. Professor Clare Bryant at the University of Cambridge is using Co-Scientist to hunt for the molecular switches that cause severe diseases, like sepsis, in humans when pathogens leap between species, and find new approaches to prevent this happening.

Testing Co-Scientist, Bryant fed it a summary of one of her grant proposals studying flu in birds and humans outlining her lab’s research questions. The tool generated and ranked a set of promising hypotheses — some she'd already considered, some she hadn't. The unfamiliar ones were the most thought-provoking.

When the grant was funded, Bryant fed in the full, detailed proposal. Later, reading through the output on a train to Brussels, she had an "a-ha!" moment: Co-Scientist had prioritised a protein that hadn’t been on her radar, connected to several signalling pathways she was already interested in. She spent the rest of the week itching to give it more data.

Back at her lab, she added unpublished material, kept confidential within Co-Scientist. With each back-and-forth, the hypotheses sharpened, moving from candidate proteins down to the specific amino acids her lab could focus its experiments on.

Bryant's team is now building cell lines containing the amino acid mutations to test the refined hypotheses. To get to the point of identifying precise amino acids would normally have taken two to three years of experimental work. But her lab is now on track to complete it in six months, she says, if the work with Co-Scientist has led them to the right targets.

来源:Google DeepMind · deepmind.google