Ai In Network Pharmcology & Modern Drug Discovery

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.