Lecture 1 - Drug Discovery and Development
Lecture 2 - Overview of Drug Discovery Workflows
Lecture 3 - Drug Design Strategies
Lecture 4 - Conventional Methods for Drug Discovery
Lecture 5 - Riddles in Drug Discovery
Lecture 6 - History and Evolution of AI in Drug Discovery
Lecture 7 - Overview of AI Technologies
Lecture 8 - Key Application of AI Across the Pipeline
Lecture 9 - Available AI Tools and Platforms
Lecture 10 - Advantages of AI Integration in Drug Discovery
Lecture 11 - Introduction to Machine Learning Concepts
Lecture 12 - Overview of Neural Networks
Lecture 13 - Feature Engineering and Data Preprocessing
Lecture 14 - Evaluation Metrics for AI Models
Lecture 15 - Introduction to Python Libraries for AI in Drug Discovery
Lecture 16 - introduction to Drug Targets
Lecture 17 - Basic of Target Identification and Validation
Lecture 18 - Omics Data Integration for Target Discovery
Lecture 19 - Binding Site and Proteins Structure Prediction with AI
Lecture 20 - Hands on Tutorial Protein Structure Prediction
Lecture 21 - Introduction and Approches to Virtual Screening
Lecture 22 - AI Tools for Virtual Screening
Lecture 23 - AI Assisted Molecular Docking
Lecture 24 - Workflow of High Throughput Virtual Screening
Lecture 25 - Hands on Tutorial - AI Assisted Molecular Docking
Lecture 26 - Basics of Lead Optimization
Lecture 27 - AI for Drug Target Interaction Studies
Lecture 28 - QSAR Modelling
Lecture 29 - Introduction to Molecular Dynamic MDSimulation
Lecture 30 - Hands on Tutorial MD Analysis
Lecture 31 - Introduction to ADMET Properties
Lecture 32 - Importance of ADMET and Its Prediction in Lead Optimization
Lecture 33 - Conventional Methods for ADMET Prediction
Lecture 34 - Openly Available Resources for ADMET Prediction
Lecture 35 - Hands on Tutorial AI Enabled ADMET Prediction
Lecture 36 - Overview of Clinical Trials
Lecture 37 - Patient Recruitment, Stratification and Retention
Lecture 38 - Clinical Trial Protocol Design and Optimization
Lecture 39 - Predicting Outcomes of Clinical Trials with AI
Lecture 40 - Data Collection and Monitoring for Regulatory Submissions
Lecture 41 - Introduction to Generative AI in Drug Design
Lecture 42 - Deep Generative Models for Drug Design
Lecture 43 - Benchmarking Generative Models for Drug Design
Lecture 44 - Molecule Optimization with Generative AI
Lecture 45 - Hands on Tutorial GNN Based De Novo Drug Design
Lecture 46 - AI in Genomics for Personalized Treatment
Lecture 47 - AI in Real Time Monitoring and Feedback
Lecture 48 - Overview and Data Sources for AI in Drug Repurposing
Lecture 49 - Integrating Multi Target Drug Discovery
Lecture 50 - network Pharmacology with AI
Lecture 51 - Public AI Resources for Drug Discovery
Lecture 52 - Success Case Studies of AI in Drug Discovery
Lecture 53 - Challenges in Modern Drug Discovery Realm
Lecture 54 - Regularory Considerations for AI Implementation
Lecture 55 - Future Outlook Explainable Artificial Intelligence XAI and Other Emerging Technologies in Drug Di
Lecture 56 - Molecular Structure Representation
Lecture 57 - Solubility Prediction Using Machine Learning
Lecture 58 - Bioactivity Prediction
Lecture 59 - Pharmacophore Based Virtual Screening
Lecture 60 - Similarity Based Screening
