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AI+ Pharma Practitioner™

Course code: NPAP1405

Formerly known as AI+ Pharma™

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

Transform Healthcare Expertise with AI+ Pharma™ for Smarter, Data-Driven Outcomes
  • Entry-Level Learning Path: Ideal for learners and professionals stepping into AI within pharmaceuticals, offering clear foundational concepts and easy-to-follow guidance
  • Integrated Learning Experience: Merges core pharma knowledge with intuitive AI tools, real-world case studies, and guided practice to build analytical and operational skills
  • Industry-Focused Development: Equips you with practical projects, situation-based exercises, and actionable insights to apply AI in drug development, research, regulatory compliance, and patient-centric solutions.
75 EUR

91 EUR including VAT

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Course dates

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 8 hours

Language: en

Price without VAT: 285 EUR

Register

Starting date: Upon request

Guaranteed

Type: Self-paced

Course duration: 8 hours

Language: en

Price without VAT: 75 EUR

Register

Starting
date		 Place
Type Course
duration		 Language Price without VAT
G Upon request Self-paced 8 hours en 285 EUR Register
G Upon request Self-paced 8 hours en 75 EUR Register
G Guaranteed course

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Course description

AI Across the Pharma Value Chain:

Understand how AI and machine learning are applied from discovery to clinical trials and post-market surveillance.

Data-Driven Drug Development:

Learn to analyze clinical, genomic, and real-world data using AI to support evidence-based drug development and decision-making.

Predictive Modeling & Patient Stratification:

Build and evaluate models for treatment outcomes, risk scoring, and optimizing trial design and recruitment.

NLP for Pharma & Healthcare Texts:

Apply NLP to extract insights from scientific literature, clinical notes, and regulatory documents.

Ethics, Regulation & Compliance:

Explore ethical, regulatory, and compliance considerations to ensure responsible and trustworthy AI use in pharma.

Course structure

Module 1: AI Foundations for the Pharma Practitioner

  • 1.1 Core AI & ML Concepts
  • 1.2 Generative AI in Pharmaceutical Workflows

Module 2: AI-Driven Drug Discovery & Molecular Design

  • 2.1 Next-Gen Molecular Drug Design 
  • 2.2 AI-Powered Drug Repurposing & Target Identification 
  • 2.3 AI for Natural Product & Peptide Discovery 

Module 3: AI-Optimized Clinical Trials

  • 3.1 AI-Enhanced Patient Recruitment 
  • 3.2 Decentralized and AI-Augmented Trial Operations 
  • 3.3 Adaptive Trial Design with AI 

Module 4: Precision Medicine, Genomics & Multi-Omics AI

  • 4.1 AI for Multi-Omics Data Integration 
  • 4.2 AI-Driven Personalized Treatment & Companion Diagnostics 
  • 4.3 AI for Rare Disease & Orphan Drug Development 

Module 5: AI in Regulatory Affairs, Medical Writing & Pharmacovigilance

  • 5.1 AI-Powered Regulatory Intelligence 
  • 5.2 Generative AI for Medical and Regulatory Writing 
  • 5.3 AI in Pharmacovigilance and Safety Surveillance 

Module 6: AI Ethics, Governance & Responsible AI in Pharma

  • 6.1 Ethical AI Principles in Pharma 
  • 6.2 AI Governance Frameworks and Compliance 

Module 7: Emerging Technologies & Future of Pharma AI

  • 7.1 AI + Quantum Computing in Drug Discovery 
  • 7.2 AI-Enabled Digital Biomarkers & Wearables 
  • 7.3 AI for Sustainable & Patient-Centric Pharma 

Module 8: Capstone Project

  • 8.1 Capstone Project 1 – AI-Driven Drug Repurposing for Rare Diseases 
  • 8.2 Capstone Project 2- AI-Powered Patient Stratification for Adaptive Clinical Trials Using a Clinical Trials Simulator GPT 
  • 8.3 Capstone Project 3 – Predictive Pharmacovigilance Using Machine Learning 

Optional Module: AI Agents for Pharma

  • 1.1 What are AI Agents? 
  • 1.2 How Does an AI Agent Work in the Pharma Value Chain 
  • 1.3 Core Characteristics of AI Agents 
  • 1.4 Importance of AI Agents (General + Pharma) 
  • 1.5 Significance of AI Agents in Pharma 
  • 1.6 Types of AI Agents? 
  • 1.7 Applications and Trends in Pharma 
  • 1.8 Case Study: Accelerated Lead Optimization with a Generative AI Agent 

Prerequisites

  • Biology Essentials: Core knowledge of human biology concepts.
  • Pharma Basics: Awareness of medication creation and regulatory steps.
  • AI/ML Foundations: Fundamental concepts in artificial intelligence and machine learning.
  • Data Analytics Proficiency: Ability to interpret and analyze datasets effectively
  • Ethical Awareness: Recognition of ethics in AI-driven healthcare applications.

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