Machine Learning Meets CADD: Accelerating Drug Discovery in the AI Era – From Hit Identification to Lead Optimization.
Master the fusion of Computer-Aided Drug Design and Deep Learning to revolutionize pharmaceutical R&D.
Course Description
Step into the future of pharmacology with this comprehensive guide to AI-driven drug discovery. This course explores the transformative synergy between traditional Computer-Aided Drug Design (CADD) and cutting-edge Machine Learning (ML) algorithms. You will dive deep into the computational pipeline, moving from high-throughput virtual screening to the de novo design of novel chemical entities using Generative Chemistry.Throughout the curriculum, we cover the implementation of Graph Neural Networks (GNNs) for molecular property prediction and Reinforcement Learning for fine-tuning drug candidates. By integrating Quantitative Structure-Activity Relationship (QSAR) modeling with modern Deep Learning frameworks, you will learn to reduce "wet-lab" costs and drastically shorten the timeline from Hit Identification to Lead Optimization. Whether you are targeting cancer proteins or viral enzymes, this course equips you with the In-Silico tools necessary to lead the next generation of precision medicine.
What You'll Learn
"Apply Machine Learning algorithms to predict molecular bioactivity and toxicity.
Master Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD).
Utilize Neural Networks to navigate and sample the vast chemical space.
Perform automated Molecular Docking and analyze protein-ligand interactions.
Use AI-based ADMET profiling to optimize the pharmacokinetic properties of leads.
Understand the role of Transformers and Generative Adversarial Networks (GANs) in molecular design.
Evaluate Virtual Screening results using statistical metrics and consensus scoring."
Curriculum
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"Module 1: Fundamentals of CADD and the AI Revolution in Drug Discovery.
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Module 2: Molecular Representations: SMILES, Fingerprints, and Molecular Graphs.
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Module 3: Machine Learning Regression and Classification for QSAR Analysis.
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Module 4: Deep Learning for Genomics and Protein Structure Prediction (AlphaFold).
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Module 5: Virtual Screening Workflows: Integrating ML with Molecular Docking.
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Module 6: Generative Chemistry: Designing Novel Molecules with AI.
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Module 7: Multi-Parameter Optimization (MPO) and Lead Refinement.
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Module 8: Case Studies: Success Stories of AI-Designed Drugs in Clinical Trials."
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