Unlocking the Potential of Computer-Aided Drug Design (CADD): Bridging Theory and Practice
Master the synergy of computational chemistry and artificial intelligence to revolutionize drug discovery. Transform theoretical molecular insights into actionable, high-affinity drug candidates using industry-leading in silico tools.
Course Description
In an era where drug development costs exceed $1 billion, Computer-Aided Drug Design (CADD) serves as the essential bridge between multi-omics data and clinical success. This comprehensive course provides a deep dive into the digital transformation of pharmacology, focusing on both Structure-Based (SBDD) and Ligand-Based (LBDD) strategies. You will explore how Deep Learning and Generative Chemistry are replacing traditional trial-and-error methods, allowing for the rapid identification of "hits" and "leads." By blending rigorous theoretical foundations with hands-on virtual lab simulations, the curriculum covers the entire pipeline—from target identification and molecular docking to ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction. Students will gain proficiency in navigating chemical spaces using AI-driven algorithms that optimize molecular potency and selectivity. Whether you are aiming to work in Big Pharma or agile Biotech startups, this course equips you with the technical expertise to leverage in silico modeling for real-world therapeutic breakthroughs.
What You'll Learn
"The architecture of the modern AI-accelerated drug discovery pipeline,
How to perform high-throughput virtual screening to identify novel scaffolds,
Techniques for Protein-Ligand docking and scoring function optimization,
Application of Generative Adversarial Networks (GANs) for de novo drug design,
Predicting drug-likeness and toxicity using Machine Learning models,
Strategies for drug repurposing using network pharmacology and big data."
Curriculum
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The architecture of the modern AI-accelerated drug discovery pipeline,
Lesson -
How to perform high-throughput virtual screening to identify novel scaffolds,
Lesson -
Techniques for Protein-Ligand docking and scoring function optimization,
Lesson -
Application of Generative Adversarial Networks (GANs) for de novo drug design,
Lesson -
Predicting drug-likeness and toxicity using Machine Learning models,
Lesson -
Strategies for drug repurposing using network pharmacology and big data.
Lesson
