Biotechnology · Workshop

Next-Gen Drug Discovery Using AI, Molecular Docking and Virtual Screening

Discover potential drug candidates through AI-assisted protein modelling, molecular docking, and virtual screening workflows.

ModeVirtual / Online
LevelModerate
Fee₹2,499

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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.

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

Hands-On Tools: UniProt, RCSB PDB, AlphaFold Protein Structure Database, ChatGPT/Gemini, Google Sheets 📅 Day 2 – Ligand Selection, Protein Preparation and Molecular 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

Hands-On Tools: PubChem, SwissDock / CB-Dock2 / AutoDock Vina-based workflow, Google Sheets 📅 Day 3 – Docking Result Visualization, Interaction Analysis and Virtual Screening

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

Hands-On Tools: PyMOL, Discovery Studio Visualizer, ChatGPT/Gemini, Google Sheets

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.

Next-Gen Drug Discovery Using AI, Molecular Docking and Virtual Screening

Next-Gen Drug Discovery Using AI, Molecular Docking and Virtual Screening

About Workshop

Aim

What Participants Will Learn

Structure

Important Dates

Registration Ends

Workshop Dates

What You Will Gain

Outcomes

Who Should Attend

Dr. Abhimanyu

Department of Biotechnology