MicrosoftExpandedEvidence: Low35/100

EY accelerates drug discovery and development in pharma

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.

Organization
EY
Industry
Pharma
Published
June 2025

Reported outcomes

Time: Up to 50% lower

Time & speed

Planned next steps

  • The source says the organization aims to achieve: Improved resource allocation for research.
Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Cost: 44-67%

ey.comJun 10, 2025UnknownInferred claimLow evidence strength

Expected cost reductions of 44%-67% in drug discovery and development.

Normalized claim

Time: 50% decrease

ey.comJun 10, 2025UnknownInferred claimLow evidence strength

Drug discovery timelines reduced by up to 50%.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
Customer
EY
Provider
Microsoft
Maturity
Unknown
Linked source
ey.com

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

  • 1GenAI-driven drug target identification and molecule creation
  • 2Automated toxicity and clinical trial design using AI
  • 3AI-powered regulatory submission automation
  • Implementation of GenAI to accelerate molecule creation, target identification, and compound screening.
  • Adoption of deep learning for drug-target interaction prediction and drug repurposing.
  • Automated workflows for regulatory compliance, reporting, and documentation generation.
  • Enrichment of clinical trial design through advanced data analytics.
  • Strategic support and change management from EY to enable successful GenAI deployments.
Technologies
  • Drug discovery timelines reduced by up to 50%.
  • Improved resource allocation for research priorities.
  • Accelerated regulatory submissions and increased trial success rates.
Sources & evidence1
Evidence: Low35/100Evidence strength
  • Customer explicitly identified
  • Quantified outcome available
  • Technical implementation details available
ExpandedExpanded

The same organization appears in newer AI deployment evidence.

  • Same organization re-documented as recently as 2026.

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

Published: Jun 10, 2025Publisher: ey.com

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

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