Course Overview
TOPAI+ Pharma Practitioner™ uncovers how artificial intelligence is transforming every stage of the pharmaceutical lifecycle — from discovery and formulation to clinical operations and commercial decision-making. The course highlights how AI accelerates molecule screening, streamlines trial design, enhances regulatory workflows, and improves patient-centric strategies. Learners explore real-world applications such as digital biomarkers, predictive modeling, automation in R&D, and AI-powered pharmacovigilance.
Scheduled Classes
TOP| 07/16/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 08/13/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 09/17/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 10/15/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 11/19/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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| 12/17/26 - NVT - Virtual Classroom - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOP- Understand AI applications across drug discovery, development, manufacturing, and commercialization
- Analyze how machine learning models support molecule prediction, target identification, and formulation
- Explore AI-driven innovations in clinical trial design, patient recruitment, and real-time analytics
- Examine regulatory compliance, ethical considerations, and risk mitigation strategies in AI-enabled pharma
- Apply AI tools and workflows to solve practical pharmaceutical challenges and optimize decision-making
Outline
TOPAI Foundations for Pharma
- AI and Machine Learning Basics
- AI Algorithms and Models
- Use Case: Predictive Modeling for Adverse Drug Reactions and Drug-Drug Interactions Using Historical Patient Datasets
- Hands-on: Build Predictive Models Using No-Code Tool (Teachable Machine)
AI in Drug Discovery and Development
- AI in Molecular Drug Design
- AI in Drug Repurposing
- Use Case: AI-Driven Drug Repurposing Successes (COVID-19 Therapeutics)
- Hands-On: Practical AI-Driven Molecular Design and Drug Repurposing Using Orange Data Mining Tool
- Hands-On 2: Exploring Disease-Drug Associations with EpiGraphDB
Clinical Trials Optimization with AI
- AI-Enhanced Patient Recruitment
- Clinical Data Management and Monitoring
- Use Case: Pfizer’s AI-Driven Analytics for Optimizing Clinical Trials
- Hands-on: Implementing Clinical Data Analytics Using No-Code Platforms (KNIME)
Precision Medicine and Genomics
- Personalized Treatment Strategies
- Biomarker Discovery
- Case Study: AI-Assisted Biomarker Discovery and Validation in Cancer Treatments
- Hands-on: Hands-On Genomic Analysis – Exploring AI-Driven Genomic Interpretation Using CBioPortal
Regulatory and Ethical AI in Pharma
- Ethical Considerations and AI Governance
- AI Compliance and Regulatory Frameworks
- Case Study: Analyzing Ethical and Regulatory Challenges Encountered in Major AI-Driven Pharma Initiatives
- Hands-on: Developing AI Governance Strategies Based on Ethical Frameworks
- Hands-on: Literature Mining with LitVar 2.0
Prerequisites
TOPRequired
- 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
Who Should Attend
TOP- Healthcare Professionals
