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Accueil › AI Use-Case Library › AI in drug discovery and science: 15 real deployments
Pharma pipelines, national labs and research groups running AI on discovery and analysis.
Scientific AI has moved from papers to infrastructure: dedicated supercomputers, multi-agent hypothesis systems in national laboratories, and models designing proteins that wet labs then validate.
Used Claude (Mythos Preview and Opus 4.8) to design protein binders against research targets validated by Adaptyv Bio and Twist Bioscience; Opus 5 processed raw NMR and LC-MS data
Reported: designed protein binders against 14 of 15 targets tested, hitting 22-35% success versus the typical 10-15% industry rate; Opus 5 processed raw NMR and LC-MS data in 23 and 19 minutes with purity within 0.1% of the lab's own reading
Using AI to generate medical systematic reviews and peer review services while advertising them as '100% human-written, never AI', with fabricated PhD staff credentials
Four US national laboratories run Meta vision models on secure infrastructure to analyze scientific imagery for the Genesis Mission.
Reported: Deployed on 300 A100 GPUs, the pipeline reduced grapevine drought-resilience analysis from about one month to roughly 15 minutes.
Google DeepMind's Co-Scientist, a multi-agent Gemini system that autonomously generates, debates, and refines scientific hypotheses, is deployed to all 17 U.S. DOE National Laboratories under the White House's Genesis Mission.
Reported: Early applications include novel drug repurposing candidates
WSU and Google built a fully automated AI pipeline for wildlife camera-trap analysis, tested across Washington, Glacier National Park and Guatemala's Maya Biosphere Reserve.
Reported: Camera-trap analysis cut from seven months to under a week; matched human ecologists in 85-90% of cases.
Using ChatGPT and AlphaFold to design personalized mRNA cancer vaccines for dogs, with clinical trials underway
Eli Lilly runs AI-driven drug discovery at pharma scale on dedicated supercomputing infrastructure under Chief AI Officer Thomas Fuchs.
Amgen, Moderna, the Allen Institute, and Thermo Fisher received OpenAI's GPT-Rosalind vertical reasoning model as a research preview for drug discovery and genomics work.
Novo Nordisk partnered with OpenAI to overhaul drug development, with pilots starting immediately in R&D, manufacturing, and commercial and full integration planned by year-end.
On-orbit image analysis using Gemma open-weight models
Loft Orbital ran Google DeepMind's Gemma 3 vision-language model on an Nvidia Jetson Orin AGX aboard its YAM-9 satellite, processing natural-language queries against live Earth imagery entirely onboard, the first reported VLM deployment in space.
C2S-Scale cell-analysis model built on Gemma
used an unreleased research version of Claude running inside Claude Code with multi-agent orchestration to advance the mathematical lower bound on the fraction of Riemann zeta zeros satisfying the Riemann hypothesis
Reported: improved lower bound from 41.6% to 67.2%; model burned 31M output tokens, generated 650 initial ideas, and orchestrated approximately 60 subagents running 2,400 shell commands and thousands of numerical validation checks
opened Genesis Open Models contribution portal at Argonne National Laboratory for universities, national labs, and companies to submit AI models, data, environments, and evaluations targeting scientific workflows
Cleveland Clinic uses the Synapsis AI LLM screening platform to speed enrollment in a Phase 3 polycythemia vera trial.
Reported: identified eligible candidates faster than conventional prescreening
Every entry names the organisation and links its source. Outcome figures are quoted as reported, never estimated. Vendor announcements without a named customer are excluded. Halted and reversed deployments are kept on purpose.