Green Biotec UG Bremen Germany
Artificial Intelligence in Pharmaceutical Industry
| Artificial Intelligence in Pharmaceutical Industry | |
|---|---|
| Course Description | This course explores the transformative role of Artificial Intelligence (AI) in the pharmaceutical industry. Participants will learn how AI can revolutionize areas such as drug discovery, manufacturing, clinical trials, and pharmacovigilance. Through hands-on Python programming, machine learning, deep learning, and natural language processing (NLP), the course provides an immersive experience in integrating AI solutions into pharmaceutical workflows. |
| Recommended Books | 1. Artificial Intelligence in Drug Discovery by Nathan Brown 2. Machine Learning in Healthcare by Kevin P. Murphy 3. Deep Learning for the Life Sciences by Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande |
| Course Learning Outcomes | After completing this course, participants will: 1. Understand the fundamentals of AI and its applications in the pharmaceutical industry. 2. Apply Python programming for AI solutions in drug development and pharmacovigilance. 3. Implement machine learning and deep learning models for clinical and research applications. 4. Utilize natural language processing for data extraction and analysis in pharma-related tasks. |
| Assessment System | Quizzes: 10-15% Assignments: 5-10% Midterms: 30-40% End Semester Exam: 40-50% |
| Lecture Plan | ||
|---|---|---|
| S.No. | Description | Quizzes/Assignment |
| 1 | Introduction to Artificial Intelligence in Pharmaceuticals | |
| 2 | Python Basics: Variables, Data Types, and Operators | Assignment 1 |
| 3 | Control Structures and Functions in Python | Quiz 1 |
| 4 | Machine Learning Overview: Supervised and Unsupervised Learning | |
| 5 | Machine Learning Algorithms: Regression, Classification, and Clustering | Assignment 2 |
| 6 | Introduction to Deep Learning: Neural Networks and Activation Functions | |
| 7 | Deep Learning Applications in Drug Discovery | |
| 8 | Building Neural Networks with Python Libraries (TensorFlow, Keras) | Assignment 3 |
| 9 | Natural Language Processing (NLP): Text Mining and Sentiment Analysis | Quiz 2 |
| 10 | NLP Applications in Pharmacovigilance | Quiz 4 |
| 11 | AI Case Studies: Drug Repurposing and Market Analysis | |
| 12 | Ethical and Regulatory Considerations for AI in Pharma | Assignment 4 |
| 13 | Future Trends in AI and Pharmaceuticals | Quiz 3 |
| 14 | Final Project: AI Solution for a Pharma Industry Challenge | |
| 15 | Project Presentations and Feedback | |
