AI+ Pharma™

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

Certificate Code: AP 1405

About This Course

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

Certificate Overview

Included

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

Duration

Instructor-Led: 1 day (live or virtual)
Self-Paced: 8 hours of content

Prerequisites

Requires basic biology knowledge, familiarity with pharmaceutical development and regulatory fundamentals, foundational understanding of AI and machine learning, essential data analytics skills, and strong awareness of ethical considerations in AI-powered healthcare.

Exam Format

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

Course Modules

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