Key Takeaways from Gilead's Innovation Talk at re:Invent 2023

Pharmaceutical development takes over a decade, costs billions of dollars, and has a 90% failure rate before reaching market, requiring researchers to mine petabytes of data and vast scientific literature. Gilead used generative AI and AWS cloud infrastructure to transform target assessment, an early-stage drug discovery process, deploying specialized large language models to query scientific literature and databases at scale, enabling natural-language search, research summarization, molecular interaction simulation, and synthesized reports with references; it also built a data mesh architecture with over 300 published data products on a decentralized, self-service analytics model. Gilead moved 80% of its compute and storage workloads to AWS and achieved a reduction of several months (not further quantified) in the time needed for target assessment reports. As stated: "It's not just about technology, it's about transforming the way we work."

AWS is Amazon's cloud platform, used across pharma for genomics compute, SageMaker/Bedrock model training, and healthcare-and-life-sciences-specific services like Amazon HealthOmics.

This sits in the Build stage of the AI use-case lifecycle: Named examples from the foundation-model labs and platforms pharma teams use to actually construct, train and generate the model or application.