Opleiding: AI+ Pharma Practitioner™ eLearning

Formerly known as AI+ Pharma™ <br> <br> Harness AI in Pharma to speed drug discovery, optimize trials, and enable precision therapies.

Revolutionize Healthcare Expertise with AI+ Pharma Practitioner™ for Smarter, Data-Driven Decisions

• Beginner-Friendly Pathway: Ideal for learners and professionals entering the world of AI in pharmaceuticals, offering clear fundamentals and easy-to-grasp concepts
• Integrated Learning Experience: Combines core pharma knowledge with intuitive AI tools, real-world case studies, and guided practice to strengthen analytical and operational skills
• Industry-Focused Growth: Equips you with practical projects, scenario-based exercises, and actionable insights to help you apply AI in drug development, research, compliance, and patient-centric solutions

• 1.1 AI and Machine Learning Basics
• 1.2 AI Algorithms and Models
• 1.3 Use Case: Predictive Modeling for Adverse Drug Reactions and Drug-Drug Interactions Using Historical Patient Datasets
• 1.4 Hands-on: Build Predictive Models Using No-Code Tool (Teachable Machine)

• 2.1 AI in Molecular Drug Design
• 2.2 AI in Drug Repurposing
• 2.3 Use Case: AI-Driven Drug Repurposing Successes (COVID-19 Therapeutics)
• 2.4 Hands-On: Practical AI-Driven Molecular Design and Drug Repurposing Using Orange Data Mining Tool
• 2.5 Hands-On 2: Exploring Disease-Drug Associations with EpiGraphDB

• 3.1 AI-Enhanced Patient Recruitment
• 3.2 Clinical Data Management and Monitoring
• 3.3 Use Case: Pfizer’s AI-Driven Analytics for Optimizing Clinical Trials
• 3.4 Hands-on: Implementing Clinical Data Analytics Using No-Code Platforms (KNIME)

• 4.1 Personalized Treatment Strategies
• 4.2 Biomarker Discovery
• 4.3 Case Study: AI-Assisted Biomarker Discovery and Validation in Cancer Treatments
• 4.4 Hands-on: Hands-On Genomic Analysis – Exploring AI-Driven Genomic Interpretation Using CBioPortal

• 5.1 Ethical Considerations and AI Governance
• 5.2 AI Compliance and Regulatory Frameworks
• 5.3 Case Study: Analyzing Ethical and Regulatory Challenges Encountered in Major AI-Driven Pharma Initiatives
• 5.4 Hands-on: Developing AI Governance Strategies Based on Ethical Frameworks
• 5.5 Hands-on: Literature Mining with LitVar 2.0

• 6.1 AI Project Management
• 6.2 Evaluating AI Tools and ROI
• 6.3 Hands-On: Practical AI Project Management Using Airtable for Tracking, Collaboration, and Management

• 7.1 Emerging AI Technologies in Pharma
• 7.2 AI for Sustainable Healthcare
• 7.3 Case Study: Analysis of Sustainability Initiatives Driven by AI in Pharmaceutical Industry Leaders
• 7.4 Hands-on: Scenario Planning and Predictive Analytics Using Dashboards for Future-Focused Decision Making

• 8.1 Capstone Project 1: Predictive Modeling for Adverse Drug Reactions in Polypharmacy
• 8.2 Capstone Project 2: AI-Enhanced Clinical Trial Recruitment and Retention
• 8.3 Capstone Project 3: AI-Powered Drug Design for Rare Diseases
• 8.4 Capstone Project Evaluation Scheme

• Python
• TensorFlow
• PyTorch
• Scikit-learn
• Pandas
• NumPy
• SQL
• Jupyter Notebooks
• MLflow
• DataBricks
• RDKit
• DeepChem
• Biopython
• Hugging Face Transformers for Biomedical NLP
• spaCy / Clinical NLP Toolkits
• Apache Spark for Healthcare Data
• Power BI / Tableau for Clinical Dashboards

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.