AI+ Pharma™

Target group

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.

Course structure

Module 1: AI Foundations for Pharma

1. 1.1 AI and Machine Learning Basics
2. 1.2 AI Algorithms and Models
3. 1.3 Use Case: Predictive Modeling for Adverse Drug Reactions and Drug-Drug Interactions Using Historical Patient Datasets
4. 1.4 Hands-on: Build Predictive Models Using No-Code Tool (Teachable Machine)

Module 2: AI in Drug Discovery and Development

1. 2.1 AI in Molecular Drug Design
2. 2.2 AI in Drug Repurposing
3. 2.3 Use Case: AI-Driven Drug Repurposing Successes (COVID-19 Therapeutics)
4. 2.4 Hands-On: Practical AI-Driven Molecular Design and Drug Repurposing Using Orange Data Mining Tool
5. 2.5 Hands-On 2: Exploring Disease-Drug Associations with EpiGraphDB

Module 3: Clinical Trials Optimization with AI

1. 3.1 AI-Enhanced Patient Recruitment
2. 3.2 Clinical Data Management and Monitoring
3. 3.3 Use Case: Pfizer’s AI-Driven Analytics for Optimizing Clinical Trials
4. 3.4 Hands-on: Implementing Clinical Data Analytics Using No-Code Platforms (KNIME)

Module 4: Precision Medicine and Genomics

1. 4.1 Personalized Treatment Strategies
2. 4.2 Biomarker Discovery
3. 4.3 Case Study: AI-Assisted Biomarker Discovery and Validation in Cancer Treatments
4. 4.4 Hands-on: Hands-On Genomic Analysis – Exploring AI-Driven Genomic Interpretation Using CBioPortal

Module 5: Regulatory and Ethical AI in Pharma

1. 5.1 Ethical Considerations and AI Governance
2. 5.2 AI Compliance and Regulatory Frameworks
3. 5.3 Case Study: Analyzing Ethical and Regulatory Challenges Encountered in Major AI-Driven Pharma Initiatives
4. 5.4 Hands-on: Developing AI Governance Strategies Based on Ethical Frameworks
5. 5.5 Hands-on: Literature Mining with LitVar 2.0

Module 6: Implementing AI in Pharma Projects

1. 6.1 AI Project Management
2. 6.2 Evaluating AI Tools and ROI
3. 6.3 Hands-On: Practical AI Project Management Using Airtable for Tracking, Collaboration, and Management

Module 7: Future Trends and Sustainability in Pharma AI

1. 7.1 Emerging AI Technologies in Pharma
2. 7.2 AI for Sustainable Healthcare
3. 7.3 Case Study: Analysis of Sustainability Initiatives Driven by AI in Pharmaceutical Industry Leaders
4. 7.4 Hands-on: Scenario Planning and Predictive Analytics Using Dashboards for Future-Focused Decision Making

Module 8: Capstone Project

1. 8.1 Capstone Project 1: Predictive Modeling for Adverse Drug Reactions in Polypharmacy
2. 8.2 Capstone Project 2: AI-Enhanced Clinical Trial Recruitment and Retention
3. 8.3 Capstone Project 3: AI-Powered Drug Design for Rare Diseases
4. 8.4 Capstone Project Evaluation Scheme

Certification

50 questions, 70% passing, 90 minutes, online proctored exam