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100% fundable

AI+ Pharma Practitioner™

AI is fundamentally reshaping pharmaceutical research. Precise data analysis, automated workflows, and generative models open new possibilities for drug design, clinical trials, and regulatory documentation – reliably and efficiently.

Key topics

  • Analyzing and leveraging pharmaceutical data with AI.
  • Automating preclinical and clinical processes.
  • Using generative models in drug discovery.
  • Managing quality and risk assessment through predictive analytics.
  • Optimizing regulatory documentation with modern AI tools.

Prerequisite
A foundational understanding of pharmaceutical processes and an interest in data-driven methods is all that's needed to get started.

Target group
Professionals in research, production, quality assurance, regulatory affairs, and related fields who want to integrate AI into their daily work.

Technological progress and structured processes form the foundation for innovative pharmaceutical work. AI creates sustainable pathways to advance research and development safely, quickly, and in a future-proof way.
 

course content
  • Introduction to AI and machine learning
  • AI algorithms and model types at a glance
  • Use case: Predictive models for adverse drug reactions and drug interactions using historical patient data.
  • Hands-on exercise: Building prediction models with a no-code tool (Teachable Machine).
  • AI in molecular drug design
  • AI in drug repurposing
  • Use case: AI-supported drug repurposing – successes with COVID-19 therapeutics.
  • Practical example: Molecular design and drug repurposing with the Orange Data Mining Tool.
  • Practical example 2: Exploring disease–drug correlations with EpiGraphDB.
  • AI-supported patient recruitment
  • Clinical data management and monitoring
  • Use case: AI-powered analyses by Pfizer to optimize clinical trial design.
  • Practical example: Implementing clinical data analysis using no-code platforms (KNIME).
  • Personalized treatment strategies
  • Discovery and validation of biomarkers
  • Case study: AI-assisted biomarker discovery in cancer treatment.
  • Hands-on exercise: Genomic analysis using AI-powered genome data with CBioPortal.
  • Ethical considerations and AI governance frameworks
  • AI compliance and regulatory requirements
  • Case study: Ethical and regulatory challenges in large AI-driven pharmaceutical initiatives.
  • Practical: Developing AI governance strategies based on ethical frameworks.
  • Practical: Literature research with LitVar 2.0
  • Structuring AI project management
  • Evaluating AI tools and assessing ROI
  • Practical: Tracking, coordinating, and managing AI projects with Airtable.
  • Emerging AI technologies in the pharmaceutical industry
  • AI for sustainable healthcare delivery
  • Case study: Sustainability initiatives driven by AI in leading pharmaceutical companies.
  • Practical: Scenario planning and predictive analytics with dashboards for future-oriented decisions.
  • Capstone Project 1: Predictive models for adverse drug reactions in polypharmacy.
  • Capstone Project 2: AI-supported recruitment and retention of clinical trial participants.
  • Capstone Project 3: AI-supported drug design for rare diseases.
  • Evaluation scheme for capstone projects.

Up to 100% funding for your continuing education

Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.

Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.