AWS Launches Agentic Drug Discovery-Wet Lab Pipeline

By Bio-IT World Staff

April 15, 2026 | AWS this week announced Amazon Bio Discovery, a new AI-powered application designed to help scientists design novel drugs quickly and send them to wet lab partners for synthesizing and testing.

Amazon Bio Discovery gives scientists direct access to a broad catalog of biological foundation models that are trained on vast biological datasets, the company said in the announcement blog. Scientists can converse naturally in their preferred terminology with an AI agent to select the right models for their research goals, optimize the inputs, and evaluate candidates for experimentation. Scientists can also train models on their prior experimental data to make more accurate predictions and send candidates to physical labs for synthesis and testing—with results routing back to the application for rapid iteration, creating a lab-in-the-loop experimentation cycle similar to ones developed by Recursion and Insilico Medicine.

Amazon Bio Discovery offers a benchmarked library of AI models and analysis packages, an AI agent that helps researchers design experiments, and integrated lab partners that test the most promising antibody candidates and route results back to the scientists. This feedback loop improves the next round of design.

"AI agents make powerful scientific capabilities accessible to all drug researchers, not just those with computational expertise," said Rajiv Chopra, vice president of AWS Healthcare AI and Life Sciences in the post. "These AI systems can help scientists design drug molecules, coordinate testing, learn from results, and get smarter with each experiment. This combination of cutting-edge AI and the robust, secure infrastructure AWS has built for regulated industries allows scientists to accelerate antibody discovery in ways that weren't possible before."

Amazon Bio Discovery brings enterprise-grade scale, performance, privacy, and security to researchers across all pharmaceutical, biotech, and academic research organizations. It provides complete data isolation and gives customers ownership over all their proprietary data and intellectual property while offering scientists a broad catalog of AI models for drug discovery, including leading open-source and commercial models from partners like Apheris and Boltz, with Biohub and Profluent coming soon.

Amazon Bio Discovery lets scientists to securely feed prior experimental data from their organization's lab results into the application. They can use their own lab data to train custom models. All fine-tuned models remain private and accessible only to the user or their organization. For organizations that have already built their own in-house models, computational biologists can easily deploy and host those models within Amazon Bio Discovery.

More importantly, according to Amazon, an AI agent walks scientists through every step—from designing experiments to selecting the most promising AI-designed candidates for lab testing. Scientists can use natural language to create experiment recipes—step-by-step workflows that combine different models and analyses—and benchmark which model works best for their research needs. To further support model selection, an extensive and growing antibody benchmark dataset is available to show the likelihood of a drug candidate that can be manufactured easily, stay stable across a temperature range, and have suitable biological properties.

Built-in Physical Lab Partners

Once scientists identify top antibody candidates, they can send them directly to Amazon Bio Discovery's integrated network of laboratory partners who physically synthesize and test molecules. Partners including Twist Bioscience, Ginkgo Bioworks—with A-Alpha Bio coming soon—provide services with transparent pricing and turnaround times. Tests measure essential information that helps scientists decide which candidates can proceed to further development.

Lab results flow back into the organization’s application environment, keeping all data connected and improving the next design cycle. One application replaces manual handoffs and disconnected systems, closing the experimental loop.

Use Case: Memorial Sloan Kettering Cancer Center

Amazon highlighted a use case from Nai-Kong Cheung M.D., Ph.D., Enid A. Haupt Chair in Pediatric Oncology at Memorial Sloan Kettering Cancer Center (MSK). Cheung used Amazon Bio Discovery’s agent to orchestrate multiple models, designing nearly 300,000 novel antibody molecules. From there, 100,000 top candidates were sent to Twist Bioscience for testing. What typically takes up to a year using traditional design methods took weeks from designing the candidates to sending them for lab testing.

"We're glad to be able to join forces with Amazon Bio Discovery to develop the next generation of antibodies that will potentially speed up the process to help patients worldwide,” said Cheung in the posting. “As researchers, we spent 20 years just to prove that the first generation of antibody worked, and then we spent another 13 years getting it into the human form before getting FDA approval. This path has been very inefficient. Patients come here with a clock. We need results sooner.”

In addition to MSK, Bayer, the Broad Institute, Fred Hutch Cancer Center, and Voyager Therapeutics are among early adopters using Amazon Bio Discovery, the company said.