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Ai In Network Pharmcology & Modern Drug Discovery - OneDDL - 11-27-2024
![[Image: e5837784c8d753ae25404acbc26ae68b.jpeg]](https://i124.fastpic.org/big/2024/1127/8b/e5837784c8d753ae25404acbc26ae68b.jpeg)
Free Download Ai In Network Pharmcology & Modern Drug Discovery
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.91 GB | Duration: 3h 22m
Learn how AI integrates with network pharmacology to revolutionize drug discovery.
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.
Requirements
Basic understanding of biology and pharmacology concepts
Familiarity with AI or computational methods is a plus but not mandatory
Description
Are you ready to explore the future of drug discovery? This course, "AI in Network Pharmacology and Modern 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:AI Integration in Pharmacology: Understand how AI tools and techniques are applied to analyze complex biological networks.Drug Target Discovery: Learn how AI predicts drug-target interactions and identifies novel therapeutic targets.Multi-Target and Combination Therapy: Explore how AI enhances the design of drugs targeting multiple pathways and synergistic drug combinations.Drug Repurposing: Discover how AI accelerates the process of finding new uses for existing drugs.Biological Network Analysis: Gain insights into constructing and analyzing protein-protein interaction networks, signaling pathways, and disease mechanisms.Predicting Efficacy and Toxicity: Master the application of AI in predicting drug effects and minimizing risks.Why Enroll?This course is perfect for students, researchers, and professionals in the fields of pharmacology, bioinformatics, and drug development who want to stay ahead of the curve. Whether you are a beginner curious about AI's potential in healthcare or a seasoned scientist looking to deepen your expertise, this course will equip you with the knowledge and skills to thrive in the era of AI-driven medicine.Join us today and transform the way you think about drug discovery!
Overview
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
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.
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