Molecular Docking & Bioinformatics for Drug Discovery

Learn NCBI, UniProt, I-TASSER, Swiss-Model, PyRx, Discovery Studio & SwissADME for in silico drug discovery

What you'll learn

⚡ 1. Retrieve protein and nucleotide sequence data from NCBI and UniProt databases
⚡ 2. Predict 3D protein structures using I-TASSER and Swiss-Model when no experimental structure is available
⚡ 3. Validate predicted protein models using Ramachandran Plot Analysis and QMEAN quality assessment
⚡ 4. Prepare ligands and target proteins for docking, including optimization steps such as solvent removal and polar hydrogen addition
⚡ 5. Perform molecular docking using PyRx
⚡ 6. Visualize and interpret protein–ligand interactions in 2D and 3D using Discovery Studio Visualizer
⚡ 7. Conduct virtual screening to filter and identify promising lead compounds
⚡ 8. Evaluate drug-likeness and ADMET properties (absorption, distribution, metabolism, excretion, and toxicity) of candidate compounds

Requirements

❗ 1. A computer (Windows or macOS) with a stable internet connection — all databases and web-based tools used in this course (NCBI, UniProt, I-TASSER, Swiss-Model, PubChem) are freely accessible online
❗ 2. No prior programming or coding experience required — every tool used in this course has a graphical, point-and-click interface
❗ 3. A basic understanding of biology or biochemistry is helpful but not mandatory — key concepts (proteins, sequences, ligands) are explained from the ground up during the course
❗ 4. Willingness to install free software — PyRx and Discovery Studio Visualizer will need to be downloaded and installed (both free); step-by-step installation guidance is provided in the course
❗ 5. Free registration on certain servers — tools such as I-TASSER require a free account for structure prediction; instructions for signing up are included in the relevant lecture
❗ 6. No prior experience with bioinformatics or molecular docking is required — this course is designed to take you from beginner to practical proficiency

Description

This course contains the use of artificial intelligence.
Welcome to this comprehensive course on Molecular Docking and Bioinformatics for Drug Discovery.
Modern drug discovery is increasingly driven by computational approaches that let researchers analyze biological data, predict protein structures, evaluate molecular interactions, and identify promising drug candidates efficiently. This course takes you through that complete workflow, step by step, using the same databases and tools used in real pharmaceutical and academic research.
Throughout the course, you will
✨ Retrieve protein and sequence data from NCBI and UniProt, two of the world's most important biological databases
✨ Predict 3D protein structures using I-TASSER and Swiss-Model
✨ Validate predicted models using Ramachandran Plot Analysis and QMEAN quality assessment
✨ Retrieve and prepare ligands and target proteins, including optimization steps such as solvent removal and polar hydrogen addition
✨ Perform molecular docking using PyRx
✨ Visualize and analyze protein–ligand interactions in 2D and 3D using Discovery Studio Visualizer
✨ Conduct virtual screening and drug-likeness assessment to filter promising lead compounds
✨ Evaluate pharmacokinetic and toxicity properties through ADMET analysis
All lectures have been developed and recorded by a specialized academic team, and I personally supervise the learning experience to ensure clear, accurate, and high-quality guidance at every step. Practical exercises and hands-on activities are built into every module, so you apply each concept in realistic research scenarios rather than just watching demonstrations.
Whether your background is in biochemistry, biotechnology, bioinformatics, molecular biology, microbiology, genetics, pharmacy, agriculture, botany, zoology, or another life science discipline, this course will help you build computational skills that support your studies, research projects, and professional growth.
My goal is not simply to teach software tools, but to help you understand how these methods are applied in real biological and pharmaceutical research. By the end, you will be able to take a target protein from data retrieval through structure prediction, docking, and drug-likeness evaluation — a complete computational drug discovery workflow you can apply with confidence.

Who this course is for

⭐ Students and researchers in biochemistry, biotechnology, bioinformatics, molecular biology, microbiology, genetics, pharmacy, agriculture, veterinary, botany, zoology, and other life science disciplines who want to build practical, hands-on skills in computational drug discovery
⭐ Graduate students preparing thesis, dissertation, or research projects that involve protein structure prediction, molecular docking, or structure-based drug design as part of their methodology
⭐ Early-career professionals in pharmaceutical, biotech, or academic research settings looking to add structure prediction, docking, and ADMET analysis to their skill set, without needing a computer science background
⭐ Anyone with a genuine interest in computational biology — including self-learners and career-changers — who wants to understand how modern drug discovery pipelines work, from data retrieval to drug-likeness evaluation
⭐ This course is best suited for beginners to intermediate learners. No prior programming or bioinformatics experience is required — just curiosity about how computational methods are transforming biological and pharmaceutical research.

Homepage

https://www.udemy.com/course/molecular-docking-bioinformatics-for-drug-discovery

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