AI+ Pharma™ eLearning

Harness AI in Pharma™ to speed drug discovery, optimize trials, and enable precision therapies.

Revolutionize Healthcare Expertise with AI+ Pharma™ 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

Module 1: AI Foundations for Pharma

* 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) Module 2: AI in Drug Discovery and Development

* 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 Module 3: Clinical Trials Optimization with AI

* 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) Module 4: Precision Medicine and Genomics

* 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 Module 5: Regulatory and Ethical AI in Pharma

* 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 Module 6: Implementing AI in Pharma Projects

* 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 Module 7: Future Trends and Sustainability in Pharma AI

* 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 Module 8: Capstone Project

* 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 Tools you will explore

* 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

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

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