Artificial Intelligence in Drug Discovery and Development

סקירה כללית

ABOUT THE COURSE:

This 12-week course, Artificial Intelligence in Drug Discovery and Development, is designed to equipparticipants with the knowledge and skills to leverage AI in the realm of drug discovery anddevelopment which itself is a daunting, expensive, time-consuming, and resource intensive task. Theprogram starts with foundational concepts, including the drug discovery pipeline and core AI/MLtechniques, progressing to cutting-edge topics like predictive modeling, generative AI-based drugdesign, and drug repurposing.

Alongside theoretical lectures, participants will gain practical experiencewith widely used AI tools and software through hands-on tutorials. The course culminates in a miniproject, offering hands-on experience and enabling participants to apply AI-driven methodologies toreal-world challenges in drug discovery.INTENDED AUDIENCE:

Pharmacy professional, computational biologists,computational chemists, BiotechnologistsPREREQUISITES:

The participants should have basic knowledge of biology, chemistry, and pharmacology.

The keen interest in the domain of drug discovery and a basic introduction to Python programming language is desirable.INDUSTRY SUPPORT:

Pharmaceutical industry such as TCS Life Science, Dr.Reddy's Laboratories, Reliance Life Science, Suven Life Sciences Ltd

סילבוס

• 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

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נלמד על ידי

Prof. Rajnish Kumar

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נושאים

Computer Science