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Free Download Basics of Computational Drug Design and Molecular Docking
Published 12/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 47m | Size: 1.83 GB
Structure-Based Drug Design: Docking, Molecular Dynamics, Free Energy Calculations, ADMET
What you'll learn
Explain the core principles of computational drug design and molecular docking, including molecular recognition and structure-based drug discovery concepts.
Prepare proteins and ligands and perform molecular docking and virtual screening using standard computational workflows and tools.
Interpret docking, molecular dynamics, and free energy calculation results to evaluate ligand-receptor stability and binding affinity.
Assess drug-likeness, ADMET properties, and toxicity risks to support informed lead selection and optimization in drug discovery.
Requirements
This course is designed to be accessible to beginners, and no prior experience in computational drug design or molecular modeling is required. Learners with a basic understanding of biology or chemistry at the undergraduate level will find the concepts easier to grasp. Familiarity with proteins, enzymes, and small molecules can be helpful, though all essential ideas are explained clearly from the fundamentals.
Description
Basics of Computational Drug Design and Molecular Docking is a comprehensive introductory course designed to provide learners with a strong foundation in modern in silico drug discovery approaches. As drug development increasingly relies on computational tools to reduce time, cost, and experimental failure, this course equips learners with the conceptual understanding needed to navigate structure-based drug design workflows with confidence.The course begins by introducing the fundamental principles of molecular recognition, protein structure, and ligand chemistry, establishing the biological and chemical basis of drug-target interactions. Learners are guided through essential computational techniques such as virtual screening and molecular docking, with emphasis on understanding docking algorithms, scoring functions, and result interpretation rather than software-specific complexity. The course then advances into molecular dynamics simulations, where learners explore protein and ligand flexibility, system stability, and time-dependent behavior of biomolecular complexes.Further modules focus on binding free energy calculations and ADMET prediction, helping learners understand how computational methods assess binding strength, drug-likeness, pharmacokinetics, and toxicity risks. Real-world case studies in infectious diseases and cancer drug discovery illustrate how these tools are applied in practical research and pharmaceutical pipelines. Throughout the course, emphasis is placed on integrating multiple computational techniques to support rational decision-making in drug discovery.This course is designed for beginners and early-career learners from life sciences, biotechnology, pharmacy, chemistry, and related fields. By the end of the course, learners will have a clear, structured understanding of how computational drug design supports experimental research and contributes to the development of safer, more effective therapeutic candidates.
Who this course is for
This course is intended for students and early-career learners who want a clear and structured introduction to computational drug design and molecular docking. It is especially valuable for learners from life sciences, biotechnology, pharmacy, chemistry, biomedical sciences, and bioinformatics who wish to understand how computational tools are used in modern drug discovery. The course is also well suited for beginners with no prior experience in in silico methods, as all concepts are introduced from the basics and explained in a step-by-step manner. Additionally, researchers, educators, and professionals who want to build foundational knowledge or transition into computational and structure-based drug design will find this course useful for strengthening their conceptual understanding and practical decision-making skills.
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