Artificial Intelligence in Drug Discovery and Development

Overview

Explore AI applications in drug discovery, from target identification to clinical trials, with hands-on tutorials and a mini-project using Python libraries, generative models, and predictive analytics.

Syllabus

- Week 1: Introduction to Drug Discovery and AI -- Overview of the Drug Discovery and Development Pipeline -- Introduction to Artificial Intelligence and Machine Learning -- Key Challenges in Drug Discovery - Week 2: Core Machine Learning Techniques -- Supervised vs. Unsupervised Learning -- Data Preprocessing and Feature Engineering -- Evaluation Metrics for Drug Discovery - Week 3: AI Tools and Software in Drug Discovery -- Introduction to Python for AI in Drug Discovery -- Overview of Popular AI Tools (e.g., Scikit-learn, TensorFlow, PyTorch) -- Hands-on Tutorial: Implementing Basic Models - Week 4: Predictive Modeling in Drug Discovery -- Structure-Activity Relationship (SAR) Models -- QSAR Modeling for Lead Discovery -- Case Studies and Real-world Applications - Week 5: Deep Learning in Drug Development -- Introduction to Neural Networks and Deep Learning -- Deep Learning Architectures for Biological Data -- Practical Session: Building Deep Learning Models - Week 6: Generative AI for Drug Design -- Generative Adversarial Networks (GANs) -- Variational Autoencoders (VAEs) in Drug Design -- Hands-on Tutorial: Designing Molecules with Generative Models - Week 7: Drug Repurposing with AI -- Concept of Drug Repurposing -- AI Approaches to Drug Repurposing -- Case Studies: Successful AI-driven Drug Repurposing - Week 8: AI in Clinical Trials and Regulatory Considerations -- AI for Optimizing Clinical Trials -- Ethical and Regulatory Considerations in AI-driven Drug Development - Week 9: Advanced Topics in AI for Drug Discovery -- Reinforcement Learning Applications -- Multimodal Data Integration -- Future Trends in AI for Drug Discovery - Week 10: Industry Applications and Case Studies -- Industry-supported Case Studies -- Guest Lectures from Pharmaceutical Experts -- Discussion on Industry Trends and Challenges - Week 11: Mini-project Preparation -- Project Selection and Proposal Development -- Guidance on Research Methodology and Data Collection -- Workshop on Project Tools and Resources - Week 12: Final Presentations and Course Wrap-up -- Presentation of Mini-projects -- Feedback and Evaluation -- Discussion on Career Opportunities and Industry Networking - Additional Resources -- Recommended Reading Materials -- Online Resources and Communities for Continuous Learning

Taught by

Prof. Rajnish Kumar

Tags