AI+ Pharma™
Harness AI in Pharma™ to speed drug discovery, optimize trials, and enable precision therapies.
Revolutionize healthcare expertise with AI+ Pharma™, designed as a beginner-friendly pathway for learners and professionals entering the world of AI in pharmaceuticals. The course introduces clear fundamentals and easy-to-grasp concepts while delivering an integrated learning experience that combines core pharmaceutical knowledge with intuitive AI tools, real-world case studies, and guided practice. With an industry-focused approach, participants engage in practical projects and scenario-based exercises, gaining actionable insights to confidently apply AI in drug development, research, compliance, and patient-centric solutions.
Enrollment Fee:
MUR 5,980
What You'll Learn
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
Prerequisites
- Basic Biology Knowledge – Understand fundamental human biology concepts.
- Pharmaceutical Fundamentals – Familiarity with drug development and approval processes.
- AI & ML Basics – Grasp core principles of artificial intelligence.
- Data Analytics Skills – Ability to interpret and analyze datasets.
- Ethical Awareness – Understand ethics in AI-driven healthcare applications.
Exam Details
Duration:
90 minutes
Passing Score:
70% (35/50)
Format:
50 multiple-choice/multiple-response questions
Delivery Method:
Online via proctored exam platform (flexible scheduling)
Unlock Self-Paced Online Learning
- Unlock Self-Paced Online Learning
- Access learning anytime, anywhere, with built-in quizzes to measure progress.
- Enrollment Fee: MUR 5,980