London · 2027

AI in Pharmaceuticals:

Drug Discovery & Beyond.

World Pharma AI Forum

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AI DISCOVERYGENOMICS • CRISPR
mRNA
CRISPR
ADC
siRNA
CAR-T

On the Agenda

Topics Discussed

01

From Target to Candidate

Scaling AI-Driven Drug Discovery

  • Where AI is shortening hit-to-lead and candidate selection, and what has reached the clinic so far
  • What UK data assets — UK Biobank, Genomics England — change about target identification
  • How big pharma partners with AI-native biotechs: deal structures, IP and how success is judged
02

Smarter Clinical Trials

Design, Recruitment & Execution

  • Protocol optimisation, digital twins and synthetic control arms: what MHRA, EMA and FDA will accept
  • Finding patients faster through NHS data, DigiTrials and real-world evidence
  • AI-authored protocols, ICFs and study reports: where human sign-off has to stay
03

Data Foundations & Governance

AI at Enterprise Scale

  • Making legacy R&D, clinical and commercial data AI-ready: master data, lineage and quality
  • Federated and privacy-preserving approaches to patient data across trusts and borders
  • Model ownership, validation and lifecycle monitoring inside a quality system
04

Agentic AI in Regulatory & Safety

Submissions, PV & Medical Writing

  • Agents in submissions, MLR review and pharmacovigilance case processing: real deployments and results
  • Evaluating and guardrailing autonomous systems in a GxP environment
  • Preparing for the FDA and EMA Good AI Practice principles, the EU AI Act and MHRA expectations
05

Commercial & Medical Affairs

Omnichannel, Agents & the Voice Channel

  • Next best action, HCP segmentation and omnichannel orchestration that proves ROI
  • Voice and agentic AI in the field and the contact centre: MSL copilots, medical information and AE intake
  • Compliance guardrails for AI-generated content under the ABPI Code and PMCPA
06

Beyond Pilots

The AI Operating Model & Workforce

  • Why most GenAI pilots stall, and what the programmes that reached production did differently
  • Central AI team versus federated teams: governance, funding and how value is measured
  • Building AI fluency across R&D, clinical, regulatory and commercial functions