AIFPAI+ PharmaArtificial Intelligence for Pharmaceutical & Life Sciences
Course Overview
AI+ Pharma: Artificial Intelligence for Pharmaceutical & Life Sciences is a five-day programme with content aligned to the AI CERTs AI+ Pharma™ certification blueprint, designed for pharmacy and life sciences professionals, R&D, clinical and regulatory teams, and healthtech innovators.
Over five days it builds capability across AI Foundations for Pharma; AI in Drug Discovery & Developme
Key Takeaways
In today’s rapidly evolving world, AI and Blockchain technologies are transforming industries, but the skills gap is widening. Millions struggle to find accessible, real-world certification programs that truly prepare them for the future of work. The challenge is clear: How do we upskill global talent with relevant, practical knowledge that keeps pace with innovation? At AI CERTs®, we saw this urgent need and decided to act.
The Spark: Why AI CERTs® Was Born
We believe talent is everywhere, but opportunity is not. Our founders witnessed a common barrier: future-ready education in AI and Blockchain was often locked behind complicated, expensive, or outdated courses. That’s why AI CERTs® was created to offer role-based trusted artificial intelligence certificate programs and blockchain certifications that are accessible to every learner, no matter if they have a tech background or not.
Course Outline
→ The AI-in-pharma landscape: where AI creates
value across the value chain
→ Machine-learning essentials: supervised,
unsupervised, and deep learning
→ Common AI algorithms and models relevant to
life sciences
→ Pharmaceutical and healthcare data types,
sources, and quality
→ No-code AI tooling for rapid prototyping (e.g.
Teachable Machine)
→ Predictive modelling for adverse drug reactions
and drug-drug interactions
→ Hands-On Lab: Participants build a no-code
predictive model on a historical patient dataset
to flag adverse-drug-reaction risk.
→ AI in molecular drug design and lead optimisation
→ Virtual screening and AI-assisted target
identification
→ AI-driven drug repurposing and its real-world
successes
→ Cheminformatics workflows with tools such as
RDKit and DeepChem
→ Exploring disease-drug associations from
biomedical knowledge graphs
→ Shortening early-discovery cycles: opportunities
and limits
→ Hands-On Lab: Participants run an AI-driven
molecular design and drug-repurposing
workflow using an open data-mining tool.
→ AI-enhanced patient recruitment and
eligibility matching
→ Smarter trial design, site selection, and
protocol optimisation
→ Clinical data management, monitoring, and
risk-based oversight
→ AI for safety signal detection and
pharmacovigilance
→ No-code clinical analytics pipelines (e.g. KNIME)
→ Case study: AI-driven analytics that optimised
real clinical trials
→ Hands-On Lab: Participants build a clinical-data
analytics workflow to model trial recruitment and retention on a no-code platform.
→ Personalised treatment strategies and adaptive
care pathways
→ Biomarker discovery and validation with AI
→ AI-assisted genomic interpretation and analysis
→ Patient stratification and risk scoring
→ Real-world evidence analysis for treatment
outcomes
→ NLP for clinical and scientific literature mining
→ Hands-On Lab: Participants perform AIdriven
genomic interpretation and biomarker exploration using an open genomics platform.
→ Ethical considerations, fairness, and AI
governance in pharma
→ AI compliance and regulatory frameworks (GxP,
FDA/EMA, data privacy)
→ Building practical AI governance and risk management strategies
→ AI project management, tool evaluation, and ROI
→ Emerging AI technologies and sustainability in pharma
→ Scenario planning and predictive dashboards
for decision-making
→ Hands-On Lab: Participants draft an AI governance strategy and a future-focused scenario-planning dashboard for a pharma initiative.
→ Capstone project: applying the full five-day
toolkit to a realistic AI in pharmaceuticals and life sciences scenario
→ Structured peer and facilitator review of each
capstone deliverable
→ Post-programme competency assessment against the learning outcomes
→ Personal action plan for applying the learning in
the workplace
→ Programme wrap-up, key takeaways, and continued-development guidance
→ Capstone Presentation: Participants present
their AI in Pharma Capstone Project and receive structured feedback before certification.
Who Should Attend?
→ Pharmaceutical R&D Directors & Research Scientists
→ Drug Discovery & Development Professionals
→ Clinical Trial Managers & Regulatory Affairs Officers
→ Medical Affairs & Pharmacovigilance Specialists
→ Biotech & Life Sciences Executives
→ Quality Assurance & Compliance Officers in pharma
→ Supply Chain & Manufacturing Managers in pharmaceutical production
→ Data Scientists & Bioinformatics Professionals in life sciences
→ Hospital Pharmacists & Clinical Pharmacy Leaders
→ Any pharmaceutical or life sciences professional seeking AI CERTs certification in
AI-driven drug development, clinical research, and regulatory compliance