MicrosoftExpandedEvidence: Low35/100

AstraZeneca accelerates drug discovery and clinical insights with AI

Use case typeDrug discoveryUpdated Jun 13, 2026

AstraZeneca, a global pharmaceutical leader, uses advanced AI and generative AI models on Microsoft Azure to fast-track drug discovery and development at its R&D center in India. Leveraging AI-driven models for protein folding, structure-guided drug design, and antibody discovery, the company improves disease understanding, medicinal target identification, and clinical prediction accuracy. AI and ML tools extract insights from large clinical trial datasets and safety/efficacy analytics, while federated learning enables collaborative model improvement across distributed data sources without compromising privacy. The Azure infrastructure supports intensive compute needs, helping AstraZeneca reduce discovery timelines and increase success rates for new medicines, ultimately driving innovation in the pharma sector.

Organization
AstraZeneca
Industry
Pharma
Location
India
Published
October 2023

Planned next steps

  • The source says the organization aims to achieve: Improved protein and antibody design accuracy.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
Customer
AstraZeneca
Provider
Microsoft
Maturity
Unknown

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

  • 1protein folding prediction
  • 2clinical trial analytics
  • 3drug discovery automation
  • Implemented AI and GenAI models, including protein folding predictors, on Microsoft Azure for R&D acceleration.
  • Applied ML and AI for clinical trial analytics and safety/efficacy studies.
  • Leveraged federated learning for collaborative modeling across organizations while preserving data privacy.
  • Used Azure for scalable, compute- and data-intensive workloads supporting cutting-edge drug discovery.
Enhanced rates of successful drug candidates through advanced simulation and analytics.
Sources & evidence1
Evidence: Low35/100Evidence strength
  • Customer explicitly identified
  • Technical implementation details available
  • Recent evidence check available
  • Last evidence check: Jun 1, 2026.
ExpandedExpanded

The same organization appears in newer AI deployment evidence.

  • Same organization re-documented as recently as 2026.
  • Cited source last checked Jun 1, 2026 — ok (0/1 broken).

Measures whether this deployment's public evidence persists — not whether the system is still in production.

Published: Oct 11, 2023Publisher: analyticsindiamag.com

AI-generated summary. Verify important details with the linked sources before relying on this case.

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