On Apr. 14, 2026, Amazon announces launching AI BioDiscovery Contract Research Orgainzation. Lab-in-the-loop drug discovery has transformed research for some organizations. AI-powered predictions improve continuously through wet-lab feedback, accelerating the path from hypothesis to validated candidates. But for most research teams, the reality looks different.

The field is moving fast. New biological AI models emerge constantly, each with different strengths, data requirements, and integration needs. Computational biologists are expected to evaluate and operationalize these models while supporting a growing number of discovery programs, often without the infrastructure or resources to match the demand. Meanwhile, bench scientists bring deep biological expertise to their targets and experiments but lack direct access to the computational tools that could accelerate their work. The result is a collaboration bottleneck: not because the science isn’t available, but because the tooling doesn’t support how these teams need to work together.

Even when systems are running, computational predictions and wet-lab workflows stay disconnected. Manual handoffs introduce delays, make it harder to reproduce experiments, and slow the feedback loop that makes lab-in-the-loop valuable in the first place. Scaling across multiple discovery programs and research teams remains a persistent challenge.

Amazon Bio Discovery changes this by bringing computational design and wet-lab validation together in one application. It makes lab-in-the-loop accessible and scalable across your entire research organization.

The application provides access to 40+ AI biology models with AI-guided selection. Users can also upload custom models as well as models licensed from third parties. Agentic assistants help you select the right models for your research goals, optimize configurations, and evaluate candidates for experimentation. Amazon Bio Discovery’s contract research organization (CRO) partners enable seamless wet-lab validation, with results flowing back to improve the next cycle.