Week 1: Basics of drug discovery pipeline
1. Drug discovery and development
2. Overview of drug discovery workflows
3. Drug design strategies
4. Conventional methods for drug discovery
5. Riddles in drug discovery
Week 2: Introduction to AI in drug discovery and development
1. History and evolution of AI in drug discovery
2. Overview of AI technologies
3. Key applications of AI across the pipeline
4. Available AI tools and platforms
5. Advantages of AI integration in drug discovery
Week 3: Fundamentals of AI and ML techniques
1. Introduction to machine learning concepts
2. Overview of neural networks
3. Feature engineering and data preprocessing
4. Evaluation metrics for AI models
5. Introduction to Python libraries for AI in drug discovery
Week 4: AI in target identification, prediction and validation
1. Introduction to biological targets
2. Basics of target identification and validation
3. Omics data integration for target discovery
4. Binding site and protein structure prediction with AI
5. Hands-on tutorial: Protein structure
prediction
Week 5: AI in high throughput virtual screening and lead identification
1. Introduction and approaches to virtual screening
2. AI tools for virtual screening
3. AI assisted molecular docking
4. Workflow of high-throughput virtual screening
5. Hands-on tutorial: AI-assisted molecular
docking
Week 6: AI in lead optimization and drug-target interaction
1. Basics of lead optimization
2. AI for drug-target interaction studies
3. QSAR modelling
4. Molecular dynamics simulations
5. Hands-on tutorial: Molecular dynamics
trajectory analysis
Week 7: ADMET predictive modelling in drug discovery
1. Introduction to ADMET Properties
2. Importance in lead optimization
3. Conventional methods for ADMET prediction
4. Open available resources for ADMET prediction
5. Hands-on tutorial: AI-enabled ADMET
prediction
Week 8: AI in clinical phase
1. Overview of clinical trials
2. Patient recruitment, stratification, and retention
3. Clinical trial protocol design and optimization
4. Predicting outcomes of clinical trials with AI
5. Data collection and monitoring for regulatory submissions
Week 9: De Novo Drug Design using Generative AI
1. Introduction to Generative AI in drug design
2. Deep Generative Models for drug design (GAN, GNN, RNN, VAE etc.)
3. Benchmarking Generative Models for drug design
4. Molecule optimization with Generative AI
5. Hands-on tutorial: AI-powered de novo
drug design
Week 10: Advanced concepts: Precision medicine, Network pharmacology and Drug repurposing
1. AI in genomics for personalized treatments
2. AI in real-time monitoring and feedback
3. Overview and data sources for AI in drug repurposing
4. Integrating multi-target drug discovery
5. Network pharmacology with AI
Week 11: Case studies, challenges, future directions, and resources
1. Public AI resources for drug discovery
2. Examples of notable successful case studies
3. Challenges in modern drug discovery realm
4. Regulatory considerations for AI implementation in drug development
5. Future outlook: Explainable artificial intelligence, (XAI) and other emerging technologies in drug discovery
Week 12: Hands-on sessions (Implementing an advanced workflow for molecular structure representation, property prediction, and ultra-large virtual screening)
1. Molecular structure representation
2. ML-assisted solubility prediction
3. AI-assisted bioactivity prediction
4. Pharmacophore- based ultra-large virtual screening
5. Similarity based virtual screening
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