MicrosoftExpanded

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

67%

costCost savings

44%cost−50%time

Strategic outcomes

New product / capabilityAccelerated molecule creation and screeningRisk & complianceAutomated regulatory compliance and documentationBetter decisions & insightImproved resource allocation for researchSpeed & agilityFaster drug discovery and development

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
  • Traditional drug discovery can take a decade or more and cost up to $2 billion per new therapy.
  • High attrition rates—only 10% of compounds advance to clinical trials.
  • Complex data analysis requirements exceed dashboard and manual capabilities.
  • Resource-draining administrative tasks like regulatory compliance and documentation.
  • Smaller biopharma companies lack in-house capabilities to implement advanced AI.
  • 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
  • Expected cost reductions of 44%-67% in drug discovery and development.
  • Drug discovery timelines reduced by up to 50%.
  • Improved resource allocation for research priorities.
  • Accelerated regulatory submissions and increased trial success rates.
Sources & evidence1
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.

Groundedness: Unavailable

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