Course10310668160 lectures
Artificial Intelligence in Drug Discovery and Development
Prof. Rajnish Kumar · IIT (BHU) Varanasi
About
This 12-week course, Artificial Intelligence in Drug Discovery and Development, is designed to equip participants with the knowledge and skills to leverage AI in the realm of drug discovery and development which itself is a daunting, expensive, time-consuming, and resource intensive task. The program starts with foundational concepts, including the drug discovery pipeline and core AI/ML techniques, progressing to cutting-edge topics like predictive modeling, generative AI-based drug design, and drug repurposing. Alongside theoretical lectures, participants will gain practical experience with widely used AI tools and software through hands-on tutorials. The course emphasizes on hands-on sessions, offering practical experience and enabling participants to apply AI-driven methodologies to real-world challenges in drug discovery.
Intended audience: Pharmacy professional, computational biologists, computational chemists, Biotechnologists
Prerequisites: 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.
Lectures
Week 01
- Drug Discovery & Development
- Overview of Drug Discovery Workflows
- Drug Design Strategies
- Conventional Methods for Drug Discovery
- Riddles in Drug Discovery
Week 02
- History & Evolution of AI in Drug Discovery
- Overview of AI Technologies
- Key Application of AI Across the Pipeline
- Available AI Tools & Platforms
- Advantages of AI Integration in Drug Discovery
Week 03
- Introduction to Machine Learning Concepts
- Overview of Neural Networks
- Feature Engineering & Data Preprocessing
- Evaluation Metrics for AI Models
- Introduction to Python Libraries for AI in Drug Discovery
Week 04
- introduction to Drug Targets
- Basic of Target Identification and Validation
- Omics Data Integration for Target Discovery
- Binding Site and Proteins Structure Prediction with AI
- Hands on Tutorial Protein Structure Prediction
Week 05
- Introduction & Approches to Virtual Screening
- AI Tools for Virtual Screening
- AI Assisted Molecular Docking
- Workflow of High Throughput Virtual Screening
- Hands on Tutorial - AI Assited Molecular Docking
Week 06
- Basics of Lead Optimization
- AI for Drug Target Interaction Studies
- QSAR Modelling
- Introduction to Molecular Dynamic MDSimulation
- Hands on Tutorial MD Analysis
Week 07
- Introduction to ADMET Properties
- Importance of ADMET & Its Prediction in Lead Optimization
- Conventional Methods for ADMET Prediction
- Openly Available Resources for ADMET Prediction
- Hands on Tutorial AI Enabled ADMET Prediction
Week 08
- Overview of Clinical Trials
- Patient Recruitment, Stratification & Retention
- Clinical Trial Protocol Design & Optimization
- Predicting Outcomes of Clinical Trials with AI
- Data Collection & Monitoring for Regulatory Submissions
Week 09
- Introduction to Generative AI in Drug Design
- Deep Generative Models for Drug Design
- Benchmarking Generative Models for Drug Design
- Molecule Optimization with Generative AI
- Hands on Tutorial GNN Based De Novo Drug Design
Week 10
- AI in Genomics for Personalized Treatment
- AI in Real Time Monitoring & Feedback
- Overview & Data Sources for AI in Drug Repurposing
- Integrating Multi Target Drug Discovery
- network Pharmacology with AI
Week 11
- Public AI Resources for Drug Discovery
- Success Case Studies of AI in Drug Discovery
- Challenges in Modern Drug Discovery Realm
- Regularory Considerations for AI Implementation
- Future Outlook Explainable Artificial Intelligence XAI & Other Emerging Technologies in Drug Di
Week 12
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT (BHU) Varanasi
- Department
- Chemical Engineering
- Teacher
- Prof. Rajnish Kumar
- Level
- Undergraduate
- Language
- English
- Subjects
- Chemistry, Engineering
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge. Coordinated by IIT Madras.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed