Green Biotec UG Bremen Germany
Artificial Intelligence in Phamaceutical Industry
| Artificial Intelligence in Pharmaceuticals | |
|---|---|
| Course Description | This comprehensive course explores the transformative role of Artificial Intelligence (AI) in the pharmaceutical industry. Students will learn how AI is revolutionizing drug discovery, development, and patient care. The course covers a wide range of AI applications across the pharmaceutical value chain, from accelerating drug discovery to enhancing drug safety monitoring and personalizing treatments. |
| Key Focus Areas | – Fundamentals of AI and its relevance to pharmaceutical research and development – Machine learning techniques for drug discovery and lead optimization – Deep learning applications in medical image analysis and diagnostic support – AI-driven approaches to clinical trial design and patient recruitment – Predictive modeling for drug efficacy and safety assessment – Natural language processing for mining scientific literature and patient records – AI in pharmacovigilance and post-market surveillance – Ethical considerations and regulatory challenges in AI-driven pharmaceutical innovation |
| Course Learning Outcomes | After completing this course, students will be able to: 1. Understand the fundamental concepts of AI and their applications in pharmaceuticals 2. Apply machine learning and deep learning techniques to pharmaceutical data 3. Develop AI models for drug discovery, clinical trials, and patient care 4. Evaluate the ethical and regulatory implications of AI in pharmaceuticals 5. Analyze emerging trends and future prospects of AI in the pharmaceutical industry |
| Assessment System | Assignments: 40% Midterm Project: 25% Final Project: 35% |
| Lecture Plan | ||
|---|---|---|
| Week | Topic | Assessment |
| 1 | Introduction to Artificial Intelligence in Pharmaceuticals | Assignment: Research a pharmaceutical industry challenge and suggest AI solutions |
| 2-3 | Python for AI: Basics, Control Structures, Functions and Modules | Hands-on assignments: Basic calculations, decision-making algorithms, mathematical functions |
| 4-6 | Machine Learning with Python: Supervised, Unsupervised, and Reinforcement Learning | Assignments: Implement regression, classification, and clustering models |
| 7 | Machine Learning in the Pharma Industry | Assignment: Analyze the role of ML in the pharma industry |
| 8-10 | Deep Learning: Neural Networks, CNNs, Data Preprocessing | Assignments: Implement neural networks and CNNs, data preprocessing |
| 11 | Deep Learning in the Pharma Industry | Midterm Project: Case study on computer vision applications in pharmaceuticals |
| 12-13 | Ethical, Legal, and Regulatory Aspects of AI in Pharmaceuticals | Assignment: Analyze a case study on AI ethics in pharmaceuticals |
| 14 | Emerging Trends and Future of AI in Pharmaceuticals | Final Project: Propose an innovative AI application for a pharmaceutical challenge |
| 15 | Final Project Presentations | Final Project Presentations |
