Download AI in Network Pharmcology & Modern Drug Discovery
AI in Network Pharmcology & Modern Drug Discovery video course download, learn how AI integrates with network pharmacology to revolutionize drug discovery. is designed to take you on an exciting journey through the cutting-edge intersection of artificial intelligence (AI), systems biology, and pharmacology. In this course, you’ll discover how AI is transforming the way we approach complex biological systems and revolutionizing drug development. From identifying novel drug targets to predicting off-target effects, you’ll learn how network pharmacology and AI work together to create safer, more effective therapies for challenging diseases.
What you’ll learn
- The role of AI tools and technologies in identifying novel drug targets and candidates.
- Fundamentals of Network Pharmacology and its significance in modern drug discovery.
- Protein-Protein Interaction Networks and their importance in disease complexity.
- Advanced concepts like Polypharmacology, Drug Target Interaction Prediction, and Combination Drug Therapy.
- Hands-on demos and case studies showcasing real-world applications of AI in drug discovery.
- Future trends, ethical considerations, and regulatory frameworks shaping this innovative field.
Course content
- Section 1: Introduction
- Lecture 1 AI in Network Pharmacology course overview
- Lecture 2 Introduction to Network Pharmacology
- Lecture 3 What is Network Pharmacology
- Section 2: Traditional Drug Discovery & Emergence of Network Pharmacology
- Lecture 4 Limitations of traditional drug discovery
- Lecture 5 Emergence of Network pharmacology
- Section 3: Network Based Drug Discovery
- Lecture 6 Network based Drug Discovery
- Lecture 7 The Human Interactome
- Lecture 8 Protein-Protein Interaction Network
- Lecture 9 Pathway and signalling Network
- Section 4: Identifying Disease complexity and Polypharmacology
- Lecture 10 Identifying Disease complexity
- Lecture 11 Polypharmacology
- Section 5: Drug Target Identification and Prediction
- Lecture 12 Identifying Drug Target
- Lecture 13 Predicting Drug Target Interaction
- Section 6: Network Based Drug Repositioning
- Lecture 14 Network based drug Repositioning
- Section 7: Combination Drug therapy and synergistic drug combinations
- Lecture 15 combination Drug therapy
- Lecture 16 Identification of synergistic drug combination
- Lecture 17 Predicting off target effect
- Section 8: Computational methods
- Lecture 18 computational methods in network pharmacology
- Lecture 19 Network motifs and modules
- Section 9: Applications, challehges and future Directions of Network Pharmacology
- Lecture 20 Applications of network pharmacology
- Lecture 21 challenges and future directions
- Section 10: Introduction to Artificial Intelligence
- Lecture 22 Introduction to artificial intelligence
- Lecture 23 AI in Drug Discovery and Development
- Lecture 24 Identifying Novel Drug candidate
- Section 11: Ethical consideration and regulatory framwork
- Lecture 25 Ethical consideration and future of AI in Drug Discovery
- Lecture 26 Regulatory framework
- Lecture 27 Role and application of AI in Network Pharmacology
- Section 12: Network pharmacology data sources and automated hypothesis generation
- Lecture 28 Network pharmacology data sources and automated hypothesis generation
- Lecture 29 AI Driven experimental validation
- Section 13: AI Driven experimental validation
- Lecture 30 AI Driven experimental validation
- Section 14: Case studies and hands on demo
- Lecture 31 Case studies and hands on demo
Who this course is for:
- Pharmaceutical and Biotechnology Researchers.
- Life Science, Bioinformatics, and Computational Biology Students.
- Healthcare Professionals interested in personalized medicine.
- AI and Data Science Enthusiasts exploring healthcare applications.
- Industry professionals seeking insights into cutting-edge drug discovery methods.
Course details
- Video quality: MP4 | Video: h264, 1280 × 720
- Audio quality: Audio: AAC, 44.1 KHz, 2 Ch
- Video duration: 3h 22m
- Update: 11/2024
- Number of lessons: 14 sections, 31 lectures
- Language: English
- Compressed file size: 1.9 GB
