Artificial Intelligence

Category: Life Sciences

AWS generative AI deviation management workflow showing data flow between services, security, and storage components

MSD explores applying generative Al to improve the deviation management process using AWS services

This blog post has explores how MSD is harnessing the power of generative AI and databases to optimize and transform its manufacturing deviation management process. By creating an accurate and multifaceted knowledge base of past events, deviations, and findings, the company aims to significantly reduce the time and effort required for each new case while maintaining the highest standards of quality and compliance.

Build a biomedical research agent with Biomni tools and Amazon Bedrock AgentCore Gateway

In this post, we demonstrate how to build a production-ready biomedical research agent by integrating Biomni’s specialized tools with Amazon Bedrock AgentCore Gateway, enabling researchers to access over 30 biomedical databases through a secure, scalable infrastructure. The implementation showcases how to transform research prototypes into enterprise-grade systems with persistent memory, semantic tool discovery, and comprehensive observability for scientific reproducibility .

Gxp Risk Based Approaches

A guide to building AI agents in GxP environments

The regulatory landscape for GxP compliance is evolving to address the unique characteristics of AI. Traditional Computer System Validation (CSV) approaches, often with uniform validation strategies, are being supplemented by Computer Software Assurance (CSA) frameworks that emphasize flexible risk-based validation methods tailored to each system’s actual impact and complexity (FDA latest guidance). In this post, we cover a risk-based implementation, practical implementation considerations across different risk levels, the AWS shared responsibility model for compliance, and concrete examples of risk mitigation strategies.

Metagenomi generates millions of novel enzymes cost-effectively using AWS Inferentia

In this post, we detail how Metagenomi partnered with AWS to implement the Progen2 protein language model on AWS Inferentia, achieving up to 56% cost reduction for high-throughput enzyme generation workflows. The implementation enabled cost-effective generation of millions of novel enzyme variants using EC2 Inf2 Spot Instances and AWS Batch, demonstrating how cloud-based generative AI can make large-scale protein design more accessible for biotechnology applications .

How Indegene’s AI-powered social intelligence for life sciences turns social media conversations into insights

This post explores how Indegene’s Social Intelligence Solution uses advanced AI to help life sciences companies extract valuable insights from digital healthcare conversations. Built on AWS technology, the solution addresses the growing preference of HCPs for digital channels while overcoming the challenges of analyzing complex medical discussions on a scale.

Build a drug discovery research assistant using Strands Agents and Amazon Bedrock

In this post, we demonstrate how to create a powerful research assistant for drug discovery using Strands Agents and Amazon Bedrock. This AI assistant can search multiple scientific databases simultaneously using the Model Context Protocol (MCP), synthesize its findings, and generate comprehensive reports on drug targets, disease mechanisms, and therapeutic areas.

Fast-track SOP processing using Amazon Bedrock

When a regulatory body like the US Food and Drug Administration (FDA) introduces changes to regulations, organizations are required to evaluate the changes against their internal SOPs. When necessary, they must update their SOPs to align with the regulation changes and maintain compliance. In this post, we show different approaches using Amazon Bedrock to identify relationships between regulation changes and SOPs.

The following diagram illustrates the workflow of patch-level prediction tasks on a WSI

Accelerate digital pathology slide annotation workflows on AWS using H-optimus-0

In this post, we demonstrate how to use H-optimus-0 for two common digital pathology tasks: patch-level analysis for detailed tissue examination, and slide-level analysis for broader diagnostic assessment. Through practical examples, we show you how to adapt this FM to these specific use cases while optimizing computational resources.

Pillars of a DCT

Advance environmental sustainability in clinical trials using AWS

In this post, we discuss how to use AWS to support a decentralized clinical trial across the four main pillars of a decentralized clinical trial (virtual trials, personalized patient engagement, patient-centric trial design, and centralized data management). By exploring these AWS powered alternatives, we aim to demonstrate how organizations can drive progress towards more environmentally friendly clinical research practices.

Efficiently fine-tune the ESM-2 protein language model with Amazon SageMaker

In this post, we demonstrate how to efficiently fine-tune a state-of-the-art protein language model (pLM) to predict protein subcellular localization using Amazon SageMaker. Proteins are the molecular machines of the body, responsible for everything from moving your muscles to responding to infections. Despite this variety, all proteins are made of repeating chains of molecules called […]