AI+ Pharma™

• Pharmacy & Life Sciences Students: Learners who want to complement their pharma or biotech background with practical AI skills.
• Pharmaceutical & Biotech Professionals: R&D, clinical, or regulatory teams aiming to apply AI in drug discovery, trials, and safety.
• Healthcare & Medical Practitioners: Doctors, clinicians, and healthcare managers interested in AI-driven decision support and precision therapeutics.
• Data scientists & AI Engineers: Technical professionals looking to specialize in pharma, healthcare analytics, and intelligent drug development pipelines.
• Healthtech & Medtech Innovators: Entrepreneurs, product managers, and consultants building AI-powered solutions for pharma, clinical research, and digital health.

• Bridges AI and Life Sciences:Connects core AI skills with pharmaceutical R&D, clinical workflows, and regulatory realities to make you truly industry-ready.
• Speeds Drug Discovery & Development:Equips you to apply AI for target identification, molecule screening, and trial optimization, shortening development cycles.
• Enhances Decision-Making in Healthcare:Enables data-driven decisions using AI models for risk assessment, patient stratification, and treatment optimization.
• Increases Career Opportunities in Pharma & Healthtech: Positions you for emerging roles at pharmaceutical companies, biotech startups, CROs, and AI-driven health platforms.
• Prepares You for the Future of Precision Medicine:Builds the skills to contribute to personalized therapies, adaptive clinical pathways, and AI-augmented healthcare ecosystems.

Inhoud

• 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

Lesmethode

Instructor-led OR Self-paced course + Official exam + Digital badge

Kenmerken

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.