Amazon Bio Discovery brings AI drug discovery to every research team

Lab-in-the-loop drug discovery has changed research for some organizations. AI-powered predictions improve continuously through wet-lab feedback, speeding up 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 speed up 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.

How does it work?

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.

For computational biologists, this means building, modifying, and enhancing computational workflows in a no-code environment without managing infrastructure or provisioning compute for training and inference. You can ensure your workflows have standardized data processing and rigorous analysis built in, then publish them for your team to use. For bench scientists, it means running multiple experiment versions in parallel and adjusting input parameters through agentic assistance, rather than waiting for someone to build a custom solution. Both roles work from the same system, the same data, and the same results.

Let's walk through a representative antibody design workflow. This mirrors the approach used in Amazon's collaboration with Memorial Sloan Kettering Cancer Center (MSK) where Amazon Bio Discovery was used to design nearly 300,000 novel antibody candidates, filter down to the top 100,000, and send them to the wet lab for testing in weeks versus up to a year using traditional design methods.

The process involves several key steps:

• Start by exploring the catalog of 40+ AI biology models, each specialized for different aspects of antibody design
• Use AI-assisted workflow recommendations with scientific rationale
• Generate and modify computational "recipes" in a no-code environment
• Run experiments with AI agent guidance for key decisions like identifying hotspot residues
• Review AI-generated summaries and pre-filtered candidates
• Send validated candidates directly to integrated lab partners like Ginkgo Bioworks, Twist Bioscience, or A-Alpha Bio
• Receive wet-lab results automatically and use them to fine-tune models

Why does it matter?

This is where collaboration compounds. Computational biologists create reusable workflows that embed and scale their expertise in model selection, pipeline design, and analytical rigor. They encode decisions like which AI biology models to chain together, how to process and validate input data, and what quality thresholds to enforce.

Bench scientists then pick up those workflows and apply their specialized knowledge of target biology and experimental context to configure experiments, evaluate computational results, and send validated candidates to the wet lab for testing. The results flow back to refine the models, and with each cycle, the workflows become more accurate, computational biologists can support even more programs, and bench scientists gain access to increasingly powerful tools.

Projects that would have waited in a queue move forward immediately. The value multiplies over time. Even when systems are running elsewhere, computational predictions and wet-lab workflows often 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.

The context

Amazon Bio Discovery is built on the same AWS infrastructure trusted by 19 of the top 20 pharmaceutical companies. You get enterprise-grade security with data isolation to ensure your proprietary experimental data and custom-trained models remain protected within your application environment.

The platform addresses a persistent challenge in drug discovery: scaling across multiple discovery programs and research teams. When lab testing completes, the data flows back into Amazon Bio Discovery automatically. You can download results for further analysis or compare in silico predictions against wet-lab outcomes to understand prediction accuracy. The data triage and wrangling that used to require manual solutions is now handled through an experimental data registry.

Here's where lab-in-the-loop becomes real. The wet-lab results feed back into the system, allowing you to fine-tune models with your newly generated data. After a few cycles, you're identifying drug-like candidates with increasing confidence. These are candidates you're ready to take to the next stage, whether that's animal testing or pre-clinical development.

Amazon Bio Discovery is available today. The platform offers a free trial and includes free digital courses from AWS Training and Certification to help teams build skills.