Download Molecular Docking (Classic & AI-Based): Theory and Practice 2026
Molecular Docking (Classic & AI-Based): Theory and Practice, learn molecular docking (classical and AI-methods) in drug design and gain practical skills for performing docking video course download. This course provides a comprehensive introduction to Molecular Docking for Drug Design, combining essential theoretical knowledge with hands-on practical training. You will explore both conventional molecular docking techniques and emerging AI-driven approaches, gaining an understanding of how these methods are applied in modern drug discovery.
The theoretical lessons introduce the fundamental principles of molecular docking, its role in drug design, and the algorithms used to predict ligand–protein binding modes. Key topics include scoring functions, search strategies, docking accuracy, and validation through re-docking. The course also introduces AI-based docking, including generative AI concepts and the algorithms behind advanced methods such as DiffDock. Alongside the technical concepts, the lectures are designed to develop a practical intuition for molecular docking and strengthen your overall understanding of computational drug design.
The practical section puts the concepts into practice through a series of guided experiments using freely available software and online web servers. All necessary input structures, datasets, and files are provided, and each exercise includes detailed step-by-step instructions so that you can follow the complete workflow from preparation to analysis.
Theoretical Part
- Section 1 introduces the fundamental principles of molecular docking in drug discovery. You will study major docking algorithms, including search methods and scoring functions, as well as techniques for evaluating docking performance through re-docking experiments.
- Section 2 focuses on AI-powered molecular docking. This section provides an introduction to generative AI techniques and explains the core concepts and algorithms used by advanced AI docking methods, including DiffDock.
Practical Part
- Experiment 1: You will learn how to validate molecular docking accuracy through a complete re-docking workflow using AutoDock Vina. The exercise covers retrieving the target protein, preparing its structure, running the docking calculation, and evaluating the resulting docking poses.
- Experiment 2: You will carry out molecular docking for a previously untested ligand. The workflow includes ligand preparation, execution of the docking procedure, and detailed analysis of predicted binding poses and protein–ligand interactions.
- Experiment 3: You will perform AI-based molecular docking with DiffDock. After running the AI docking workflow, you will examine the predicted results and compare them with those obtained using conventional molecular docking techniques.
What you’ll learn
- Understand the main concepts of molecular docking and the applications in drug design.
- Learn the theoretical basis of docking algorithms, including scoring functions and search algorithms.
- Build and validate docking protocols using re-docking experiments.
- Perform docking of new ligands and analyze binding poses and protein-ligand interactions.
- Apply AI-based docking methods and understand the underlying generative AI algorithms.
- Enhance overall CADD skills, including protein/ligand preparation, workflow planning and execution.
Course content
- Course Overview
- Molecular Docking Concepts
- Molecular Docking Overview
- Docking Algorithms: Scoring Functions & Search Algorithms
- Docking Accuracy Validation: Re-Docking
- Experiment 1: Re-Docking Experiment
- Experiment Overview
- Protein Target Retrieval
- Performing Molecular Docking
- Docking Result Analysis
- Experiment 2: Docking New Ligand
- Experiment Overview
- Performing Docking of a New Ligand
- Al-Based Docking Methods
- Generative AI Methods Overview
- Generative AI Algorithm [Part 1]
- Generative AI Algorithm [Part 2]
- Experiment 3: Al-Based Docking Experiment
- Performing AI-Based Docking
- Docking Result Analysis
Course details
- Video quality: MP4 | Video: h264, 1280 × 720
- Audio quality: Audio: AAC, 44.1 KHz, 2 Ch
- Last updated 08/2026
- Video duration: 02h 23m
- Number of lessons: 6 sections, 15 lectures
- Language: Language: English
- Compressed file size: 1.1 GB
