What challenges did Genentech face before AI implementation?

Manually sifting through millions of biomedical data sources for biomarker validation, a process that takes years and is highly complex. Scientists faced difficulty validating biomarkers across vast scattered datasets requiring long, manual searches.

Source

Evidence: Medium50/100

Genentech leverages generative AI with Amazon Bedrock Agents to accelerate drug discovery

Use case typeLife sciences innovationUpdated Jun 13, 2026

Genentech uses Amazon Bedrock Agents with Anthropic Claude Sonnet 3.5 to automate complex biomedical research workflows for biomarker validation. The gRED Research Agent processes and synthesizes information from millions of scientific data sources using multi-agent collaboration and Retrieval Augmented Generation (RAG). This automation reduces manual research time from weeks to minutes, freeing scientists to focus on high-impact tasks and accelerating drug discovery.

Organization: Genentech
Industry: Healthcare
Location: United States
Published: April 2026

Reported outcomes

Time: More than 43 hours

Time & speed

Planned next steps

Accelerated time-to-target identification and enabled faster delivery of lifesaving medicines to patients.

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 43 hours

AWS Solutions Case StudiesApr 29, 2026Case studyInferred claimMedium evidence strength

Automated over 43,000 hours of biomarker validation, reducing data analysis time from weeks to minutes.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Genentech
Provider: AWS
Maturity: Unknown
Linked source: AWS Solutions Case Studies

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1Generative AI
2Autonomous AI Agents
3Drug Discovery Automation

Developed gRED Research Agent on AWS Amazon Bedrock, utilizing autonomous generative AI agents to break down complex queries and access multiple scientific data sources simultaneously.
Implemented multi-agent collaboration leveraging Retrieval Augmented Generation (RAG), internal APIs, and scientific databases for comprehensive, cited synthesis of scientific data.

Technologies

Amazon Bedrock Agents Anthropic Claude Sonnet 3.5 Retrieval Augmented Generation (RAG)

Automated over 43,000 hours of biomarker validation, reducing data analysis time from weeks to minutes.

Architecture

The architecture involves the gRED Research Agent built on Amazon Bedrock Agents platform using Anthropic Claude Sonnet 3.5, integrating Retrieval Augmented Generation (RAG) and multiple data sources including PubMed, Human Protein Atlas, and internal repositories. Multi-agent collaboration enables specialized sub-agents to query distinct data domains and synthesize findings with traceable citations.

Sources & evidence1

Evidence: Medium50/100Evidence strength