【AI前沿】Two AI-based science assistants succeed with drug-retargeting tasks

We can help with thatTwo AI-based science assistants succeed with drug-retargeting tasksBoth tools generate hypotheses; one goes on to analyze some of the data.John Timmer–May 19, 2026 2:55 pm|8Finding connections within the messy world of biology is central to these new tools.Credit:Andriy OnufriyenkoFinding connections within the messy world of biology is central to these new tools.Credit:Andriy OnufriyenkoText settingsStory textSizeSmallStandardLargeWidthStandardWideLinksStandardOrange Subscribers onlyLearn moreMinimize to navOn Tuesday, Nature released two papers describing AI systems intended to help scientists develop and test hypotheses. One, Google’s Co-Scientist, is designed as what they term “scientist in the loop,” meaning researchers are regularly applying their judgments to direct the system. The second, from a nonprofit called FutureHouse, goes a step beyond and has trained a system that can evaluate biological data coming from some specific classes of experiments.While Google says its system will also work for physics, both groups exclusively present biological data, and largely straightforward hypotheses—this drug will work for that. So, this is not an attempt to replace either scientists or the scientific process. Instead, it’s meant to help with what current AIs are best at: chewing through massive amounts of information that humans would struggle to come to grips with.What’s this good for?There are some distinctions between the two systems, but both are what is termed agentic; they operate in the background by calling out to separate tools. (Microsoft has taken a similar approach with its science assistant as well; OpenAI seems to be an exception in that it simplytuned an LLM for biology.) And, while there are differences between them that we’ll highlight, they are both focused on the same general issue: the utter profusion of scientific information.With the ease of online publishing, the number of journals has exploded, and with them the number of papers. It has gotten tough for any researcher to stay on top of their field. Finding potentially relevant material in other fields is a real challenge. If you’re focused on eye development, for example, one of the signaling systems used may also be involved in the kidney, and it can be easy to miss what people are discovering about it.As the people at FutureHouse put this issue, “By focusing on ‘combinatorial synthesis’ (identifying non-obvious connections between disparate fields), Robin effectively targets ‘low-hanging fruit’ that human experts may overlook due to the compartmentalization of scientific knowledge.”This is a task that’s well-suited to AI, which can chew through the peer-reviewed literature in the background while researchers do other things. This isn’t really a question of whether an AI could do something better or worse than a human; it’s more of an issue of whether any human would end up doing these sorts of searches at all.By finding enough connections among disparate research, these tools can make suggestions—hypotheses, really—about the biology. This can include things like what processes underlie biological behaviors and what pathways and networks regulate those processes. And, in the cases explored here, it included suggesting known drugs that might target some of these pathways in diseased cells: acute myeloid leukemia in Google’s case, and a form of macular degeneration for FutureHouse.Co-scientistAs you might imagine, Google’s system is based on the company’s Gemini large language model. That helps the system interpret a statement of research goals provided by human scientists and starts a literature search to find relevant information and form hypotheses. Those are then evaluated relative to each other in a “tournament,” the results of which are evaluated by a Reflection agent. An Evolution agent can then make improvements to any surviving ideas, which can be sent back through the process.Key criteria considered throughout this process include plausibility, novelty, testability, and safety. And the Reflection tool has access to external search tools, as access to the scientific literature “prevented the hallucination of seemingly novel but implausible hypotheses,” the company wrote.As the paper puts it, scientists were kept in the loop at all times. In the search for potential drugs targeting leukemia, the suggestions made by the system were prioritized based on a review by a panel of experts, who had access to the literature Co-Scientist used to formulate its suggestions.The results are what you would expect from cancer therapies. Some of the drugs identified were effective, but only against subsets of a panel of myeloid leukemia cells. That’s not unusual, given that there are multiple routes to unchecked growth, so drugs that block the route followed by one cell type may not be effective in cells that took a different route.Google also mentioned that the system could do more general hypothesizing that do