Scientists now have two AI assistants that sift through biology literature to propose drug targets. Google’s Co‑Scientist and FutureHouse’s Robin both read hundreds of papers, generate hypotheses, and let researchers decide what to test. These tools aim to turn vast, siloed data into actionable ideas, a task no human can keep pace with for tomorrow's therapies.

Co‑Scientist relies on Gemini, a large‑language model, to parse research goals, pull relevant papers, and score hypotheses in a tournament style. Human experts review the top candidates, and the system iterates through Evolution and Reflection agents. The process surfaced several leukemia‑targeting drugs, some effective on specific cell subtypes, demonstrating the model’s clinical relevance in a rapid, data‑driven cycle.

Robin, in contrast, employs specialized search tools Crow and Falcon to summarize 551 papers in 30 minutes—a fraction of human effort. It then ranks hypotheses with an LLM judge and proposes assays, drugs, and cell models for macular degeneration. Human reviewers approved most suggestions, underscoring that AI augments rather than replaces scientific judgment for future therapeutic strategies and research pipelines.