AI in Pharma R&D - Navigating efficiency, compliance and innovation in basic and preclinical (drug) development

- Overview about AI policy and regulatory frameworks - Structured content as basis for AI-based document compilation - Use case: Preparation of a GLP Final Report based on content from the GLP Study Plan/Protocol - Project insights: Combining established and innovative methods to elucidate mechanism-of-action pathways for intervention developmentAI in Pharma R&D: During the half-day seminar you will learn how to navigate compliance requirements due to regulatory frameworks, you will explore how structured content enables AI-supported document creation, and how to combine established scientific methods with AI to accelerate drug development in basic and preclinical research.LernzieleThis seminar offers a timely opportunity to deepen your understanding of artificial intelligence (AI) as it reshapes preclinical drug development.- You, as professional in Pharma R&D, will gain practical knowledge of the latest regulatory frameworks, including the EU AI Act and the joint EMA/FDA principles published in January 2026, equipping you to navigate compliance requirements with confidence.Furthermore, you will explore how structured content enables AI-supported document creation in regulated environments (use case: preparation of a GLP Final Report based on content from the GLP Study Plan/Protocol).Through real-world project insights, you will learn how to strategically combine established scientific methods with AI and explainable AI (xAI) to uncover mechanisms of action and accelerate drug discovery.- Whether you are evaluating AI adoption or looking to optimise your current approach, this course provides the critical knowledge needed to stay competitive and compliant in a rapidly evolving field.ZielgruppenIn case youre considering AI implementation, our new program provides essential insights for informed decision-making in preclinical research.This course is designed for pharmaceutical R&D professionals, including project managers, scientific researchers and regulatory affairs specialists who want to stay current with AI developments in basic and preclinical (drug) development.