About Workshop
This 3-day hands-on workshop introduces participants to the practical workflow of computational drug discovery using AI-assisted protein structure prediction, molecular docking, and virtual screening. Participants will learn how to select a disease-related target protein, retrieve protein sequences and structures, prepare ligands, perform docking analysis, and interpret protein-ligand binding interactions.
The workshop focuses on beginner-friendly and research-relevant tools such as UniProt, RCSB PDB, AlphaFold Protein Structure Database, PubChem, SwissDock, CB-Dock2, AutoDock Vina-based workflows, PyMOL, Discovery Studio Visualizer, and AI tools like ChatGPT/Gemini for research support and result interpretation.
By the end of the workshop, participants will be able to prepare a basic docking workflow, compare ligands using docking scores, visualize binding interactions, and create a mini docking analysis report suitable for academic or research use.
Aim
The aim of this workshop is to train participants in the practical use of AI-assisted and computational tools for protein structure prediction, molecular docking, and virtual screening in early-stage drug discovery research.
What Participants Will Learn
- To introduce participants to the fundamentals of computational drug discovery and molecular docking.
- To help participants understand target protein selection and disease relevance.
- To train participants in retrieving protein sequences from UniProt and 3D structures from RCSB PDB or AlphaFold.
- To demonstrate ligand selection and compound retrieval from PubChem.
- To explain protein preparation, ligand preparation, docking grid, and binding pocket concepts.
- To guide participants in performing molecular docking using beginner-friendly docking platforms.
- To help participants interpret docking scores, binding energy, and protein-ligand interactions.
- To train participants in visualizing docking poses using PyMOL or Discovery Studio Visualizer.
- To enable participants to compare multiple ligands for virtual screening and lead-like compound selection.
- To support participants in preparing a short docking result interpretation using AI tools.
Structure
📅 Day 1 – Protein Target Selection and Structure Prediction
- Introduction to computational drug discovery
- Role of molecular docking in early-stage drug discovery
- Understanding target proteins and disease relevance
- Protein sequence and structure basics
- Retrieving protein sequences from UniProt
- Retrieving 3D protein structures from RCSB PDB
- Introduction to AI-based protein structure prediction
- AlphaFold and its role in structural biology
- Basic quality checks for protein structures
- Selecting a suitable protein structure for docking
- Introduction to ligands, drug-like molecules, and natural compounds
- Retrieving ligands from PubChem
- Understanding molecular formats: PDB, SDF, MOL2, and SMILES
- Protein preparation basics
- Ligand preparation basics
- Docking grid and binding pocket concept
- Molecular docking workflow overview
- Performing docking using beginner-friendly docking platforms
- Docking score and binding energy interpretation
- Common docking errors and precautions
- Understanding protein-ligand binding interactions
- Hydrogen bonds, hydrophobic interactions, and van der Waals interactions
- Visualizing docking poses
- Using PyMOL / Discovery Studio Visualizer
- Comparing multiple ligands
- Basics of virtual screening
- Selecting lead-like compounds
- Limitations of docking analysis
- Scientific reporting of docking results
- AI-assisted result summarization and interpretation
Important Dates
Registration Ends
4:30 PM
Workshop Dates
2026-06-18
5:30 PM
5:30 PM
What You Will Gain
Outcomes
- Understand the role of protein structure prediction and molecular docking in drug discovery.
- Select a disease-related target protein for computational analysis.
- Retrieve protein sequences from UniProt and 3D structures from RCSB PDB or AlphaFold.
- Identify and retrieve ligand molecules from PubChem.
- Understand basic protein and ligand preparation steps for docking.
- Perform molecular docking using beginner-friendly docking tools.
- Interpret docking scores, binding energy, and binding pocket interactions.
- Visualize protein-ligand interactions using PyMOL or Discovery Studio Visualizer.
- Compare multiple ligands for virtual screening and identify promising lead-like compounds.
- Prepare a mini docking analysis report with AI-assisted interpretation support.
Who Should Attend
- Undergraduate and postgraduate students in Biotechnology, Bioinformatics, Life Sciences, Pharmacy, Biochemistry, Microbiology, Molecular Biology, and related fields.
- PhD scholars, researchers, and academicians working in drug discovery, computational biology, structural biology, or pharmaceutical research.
- Biotechnology and pharmaceutical professionals interested in molecular docking and virtual screening.
- Bioinformatics learners and computational biology enthusiasts who want hands-on exposure to protein-ligand docking workflows.
- Students and professionals interested in AI-assisted drug discovery, molecular modelling, and early-stage compound screening.