Pharma AI Use Case Ranking

Tangram Therapeutics is a UK biotech company focused on solving human disease through computational RNA interference (RNAi) medicines.,To accelerate drug target discovery and evaluation, Tangram built LLibra OS, an agentic AI platform that unifies proprietary, licensed, and curated public datasets for research and target-indication assessment.,The platform supports retrieval augmented generation, web search, and text-to-SQL to help researchers identify novel targets, evaluate therapeutic potential, and design medicines.

Microsoft Discovery is a new enterprise AI agent platform designed to transform research and development (R&D) across sectors. By orchestrating specialized AI agents with a sophisticated graph-based knowledge engine, researchers and scientists can reason contextually through complex data, simulate experiments, and iterate research plans faster than ever before. Built on Azure High Performance Computing (HPC) and Azure AI Foundry, it emphasizes trust, compliance, and extensibility. Real-world impact is demonstrated by Microsoft researchers rapidly developing a sustainable immersion datacenter coolant, a process that normally takes years, in just 200 hours. GSK aims to accelerate medicinal chemistry, while The Estée Lauder Companies focuses on faster product innovation. The platform integrates partner technology from NVIDIA, Synopsys, and PhysicsX to enable advances in pharma, materials, semiconductor design, and industrial engineering. Strategic alliances with Accenture and Capgemini are helping scale deployments. Microsoft Discovery is positioned as a future-proof system for solving the most challenging R&D problems using AI agents and Microsoft’s secure cloud foundation.

BenchSci transforms fragmented biomedical evidence into a graph-native model of disease biology, empowering 9 of the top 10 pharmaceutical companies to de-risk and accelerate discovery.,BenchSci’s ASCEND platform combines large language models with the Biological Evidence Knowledge Graph (BEKG), an experimentally grounded knowledge system that integrates open-access literature, closed-access publications, and proprietary pharmaceutical datasets.,Its LENS extraction engine uses Google Cloud Vertex AI and Gemini models to interpret scientific papers with contextual awareness and validate claims against associated imagery before materializing structured assertions into Neo4j.

Schrödinger uses Google Cloud as the foundation for its physics-based molecular simulation platform to accelerate drug discovery and reduce the cost and time of lab work.,The company runs compute-intensive simulation workloads that previously took up to three weeks on on-premises clusters; with Google Cloud, it can scale horizontally across more GPUs and CPUs to finish computations within hours.,Schrödinger added BigQuery as a data hub for hundreds of billions of molecules, reagents, and iterations, and uses machine learning to narrow simulation results toward the most promising candidate molecules.

Alnylam Pharmaceuticals uses generative AI to streamline product complaint management and internal knowledge access.,The company built a product complaints intake and triage prototype with Amazon Bedrock and Amazon S3, and a Slack-integrated internal assistant called AskALNY for employee information retrieval.

AstraZeneca developed Development Assistant, an AI tool using Amazon Bedrock Agents with multi-agent architecture for fast natural language querying of clinical, regulatory, safety, and quality data.,The solution unifies structured and unstructured data sources to provide transparent, actionable insights supporting faster decision-making in drug development.,Multi-agent AI architecture routes queries to specialized agents for context-aware, high-performance responses across clinical trial and R&D domains.,Development Assistant reduced insight generation time from hours to minutes and scaled to 1,000+ users, breaking down domain silos across pharmaceutical R&D.,The tool provides transparent data source referencing and is progressing toward expansion across broader R&D functions to accelerate medicine development pipeline.

Novo Nordisk, a multinational pharmaceutical company based in Denmark, wanted to democratize employee innovation by enabling secure and compliant use of generative AI in nonregulated processes.,They developed a self-service generative AI platform on AWS using Amazon Bedrock, DynamoDB, and AWS Lambda, enabling employees to build, customize, and share chatbots.,The solution reduced innovation cycle time from months to days and lowered operational costs, resulting in over 25,000 employees creating chatbots for more than 2,500 use cases across various workflows.

Merck & Co., Inc. modernized its clinical data ecosystem to overcome siloed systems and accelerate drug development and manufacturing efficiency using AWS generative AI and analytics technologies.,The company implemented a data platform spanning 300+ clinical trials and deployed AI models for medical coding, database workflows, and drug design using AWS HealthOmics and Anthropic Claude models on Amazon Bedrock.,Merck also used generative AI text-to-SQL for natural language querying of healthcare data, improving analyst efficiency and accelerating R&D decisions.,The manufacturing data analytics platform was rebuilt on AWS, leading to a 3x performance boost and 50% cost reduction.,These innovations enabled a 70% reduction in clinical trial costs, 3x faster data processing, and overall operational cost savings.

AstraZeneca is a global, science-led biopharmaceutical company focused on discovery, development and commercialization of prescription medicines.,The company uses AWS to support research through commercialization, and specifically applies Amazon Bedrock to accelerate clinical trials by combining structured and unstructured data.,An agentic AI-powered Development Assistant gives clinical, regulatory, safety, and quality teams conversational access to trusted insights in seconds.

PozeSCAF Discovery Solutions (formerly Immunocure Discovery Solutions) turned to AWS for scalable, high-performance infrastructure to optimize molecular dynamics workloads.,The company cut simulation runtimes by more than half, reduced compute costs, and accelerated its drug discovery pipeline.,It also began exploring generative AI/agentic workflows with Amazon Bedrock to build knowledge graphs from project data and flag potential side effects earlier.

Indegene built an AWS-based social intelligence platform for life sciences companies to extract actionable insights from healthcare conversations on social media at scale.,The solution addresses challenges in monitoring brand sentiment, launch reactions, adverse events, and stakeholder discussions by combining healthcare-specific NLP, governance controls, and generative AI.,The article presents a layered architecture and example query-generation workflow that supports compliance-aligned, domain-specific social listening and analysis.

Pienomial's Knolens platform delivers always-on AI for life sciences at lower prices while developers focus on products, not ops.,Knolens combines proprietary evidence-structuring technology with generative AI to help organizations work with dense scientific and regulatory information so that high-stakes decisions can move forward with confidence.,Pienomial migrated Knolens fully to Google Cloud to improve reliability, scalability, and cost predictability for time-sensitive scientific work.

SciOne AI is transforming R&D and lab operations through digitization and AI, delivering an AI-powered IDE for researchers in the chemical and life sciences industries.,The company developed more than ten AI agents for lab operations, including equipment, inventory, PLM, recipe, sample, and test agents, to streamline repetitive research workflows.,The platform uses a supervisor agent to route tasks to sub-agents, integrates customer-side tools for domain-specific scenarios, and builds a knowledge base for lab manuals, equipment documentation, and safety specifications.

Cactus Life Sciences needed to reduce the manual burden of document-heavy scientific workflows and improve efficiency across scientific writing and project management teams while meeting strict pharmaceutical data security requirements.,The company deployed Microsoft 365 Copilot in a phased rollout and built more than 30 custom automation agents using Microsoft's agent builder framework.,The agents retrieve and structure information from scientific literature, support abbreviation checks, formatting consistency, and alignment with regulatory and publication standards, with human review controls preserved.,A Copilot Champions program and centralized knowledge repository supported governance and peer-led adoption.

EY guides pharmaceutical companies through adopting generative AI (GenAI) to accelerate and automate early-stage drug discovery processes.,GenAI enables significant breakthroughs in molecule creation, compound screening, and toxicity prediction, previously requiring extensive time and costs.,EY collaborates with life sciences industry leaders, providing strategy and change management for successful GenAI implementation.,The solution leverages deep learning algorithms for virtual screening, target identification, and optimal resource allocation.,Predictions point toward cost savings from 44% to 67% and time reductions up to 50% for critical research phases, as GenAI adoption accelerates.,The methodology streamlines clinical trial design and data analysis, improves regulatory submission, and automates documentation and compliance.,EY works with both large and small biopharma companies to extend the benefits industry-wide, including for organizations lacking in-house AI capabilities.,The approach helps CDMOs and CROs differentiate using advanced AI for outsourced drug research steps.,Results reported by EY clients and survey respondents describe speed-to-market and cost reduction as primary impacts.,The article describes a transformation in research and development operating models for faster patient benefit and broader treatment diversity.

Sanofi built Concierge in-house on AWS as a unified, agentic AI companion for its global workforce, using Amazon Bedrock for model flexibility and planning to add Amazon Bedrock AgentCore for orchestration.,The system gives employees a single conversational interface for finding information, generating content, completing tasks, and discovering specialized agents across the company.

Novo Nordisk A/S uses computer vision and machine learning on AWS to automate manufacturing quality tasks such as cartridge counting and anomaly detection for agar plates.,The company built a prototyping solution to train, deploy, monitor, and manage ML models for edge devices and to support regulated pharmaceutical operations.

Researchers in the biotech and pharmaceutical industries grapple daily with the complexity and volume of single-cell RNA sequencing (scRNA-seq) datasets, which are crucial for understanding diseases and developing targeted therapies.,Sonrai sought to use generative AI to simplify and streamline interpretation of scRNA-seq data and reduce the manual burden on immunologists.,Sonrai Discovery uses large language models through Amazon Bedrock to automate cluster annotation, generate consistent text reports, and support faster analysis on AWS.

Tenthpin, a life sciences consultancy and product company, faced challenges in improving the accuracy and speed of certificate verification against specification documents for pharmaceutical manufacturing.,They developed Tenthpin Intelligent Certificate VerificAItion (T/ICV), a SaaS generative AI solution using Amazon Bedrock foundation models to extract, compare text from certificates and specifications, and automate verification workflows.,The solution integrates with SAP Business Technology Platform and uses AWS Lambda for automation and Amazon Aurora as the relational database.,T/ICV decreased certificate verification time by 95%, improved accuracy to nearly 100%, reduced onboarding time from months to weeks, and enhanced regulatory compliance risk management.

Sanofi launched its Digital Accelerator to speed up digital innovation across research and development, clinical, commercial, and manufacturing workflows.,The initiative aims to shorten the time from discovery to therapy and improve patient, provider, and employee experiences through AI-powered solutions.