Google DeepMind Co-Scientist 如何帮剑桥团队锁定新发传染病的分子开关

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Back at her lab, she added unpublished material, kept confidential within Co-Scientist.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.