AstraZeneca accelerates drug discovery and clinical insights with AI
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
Reported outcomes
Strategic outcomes
Primary read
Use case focus
Showing 3 of 3
- 1protein folding prediction
- 2clinical trial analytics
- 3drug discovery automation
- Traditional drug discovery and protein structure determination are expensive and time-consuming, slowing innovation.
- Extracting value from vast, complex biomedical datasets is resource-intensive.
- Developing accurate predictive models and collaborative research across silos is difficult due to privacy concerns.
- Clinical trials require deep analytics for safety, efficacy, and event adjudication.
- 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.
- Significantly reduced time for drug discovery and candidate identification.
- Improved accuracy of target protein and antibody design using AI models.
- Enhanced rates of successful drug candidates through advanced simulation and analytics.
- Enabled collaborative learning and innovation while upholding privacy in a regulated sector.
Sources & evidence1
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.
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