AI-Driven Drug Discovery: AlphaFold 3, Molecular Docking, ADMET & GROMACS

Aim

Workshop Structure

📅 Day 1: AlphaFold 3, Protein Structure Analysis & Drug Target Preparation

Focus: Understand AI-based protein structure prediction and prepare drug targets for computational analysis.

• Introduction to AI-driven drug discovery and structure-based drug design.
• Exploring AlphaFold 3 for protein structure and biomolecular complex prediction.
• Understanding protein structure confidence, binding pockets and structural limitations.
• Retrieving drug target information and experimental protein structures using UniProt and RCSB PDB.
• Visualizing protein structures, identifying active sites and analyzing binding pockets using PyMOL.
• Preparing a suitable experimental protein structure for molecular docking.

🛠️ Hands-On Lab: Protein Structure Analysis & Target Preparation

Explore an AlphaFold 3 example, retrieve an experimental EGFR protein structure, identify the ligand-binding pocket and prepare the protein target for docking.

🧰 Tools: AlphaFold 3 educational resources, UniProt, RCSB PDB, PyMOL.

📅 Day 2: Molecular Docking, Drug–Target Interactions & ADMET Screening

Focus: Investigate protein–ligand binding and evaluate drug-like properties for candidate prioritization.

• Introduction to molecular docking, virtual screening and ligand preparation.
• Protein and ligand preparation using AutoDock Vina, Meeko and RDKit.
• Binding-site selection, docking-grid configuration and ligand pose prediction.
• Analyzing docking scores, hydrogen bonds and hydrophobic interactions using PyMOL.
• Evaluating drug-likeness and physicochemical properties using SwissADME.
• Predicting pharmacokinetic and toxicity-related properties using ADMETlab 3.0.
• Comparing docking interactions and ADMET profiles for preliminary candidate prioritization.

🛠️ Hands-On Lab: Molecular Docking & ADMET-Based Compound Screening

Perform guided docking of three reference EGFR inhibitors, including erlotinib, gefitinib and lapatinib, using a prepared experimental receptor structure. Compare predicted binding poses, investigate molecular interactions and evaluate ADMET properties.

🧰 Tools: AutoDock Vina, Meeko, RDKit, PyMOL, SwissADME, ADMETlab 3.0.

📅 Day 3: GROMACS Molecular Dynamics, Trajectory Analysis & Drug Candidate Evaluation

Focus: Examine protein–ligand molecular dynamics and integrate computational results for drug candidate assessment.

• Introduction to molecular dynamics simulations and GROMACS.
• Understanding force fields, ligand topology and protein–ligand system preparation.
• Overview of solvation, energy minimization and NVT/NPT equilibration.
• Demonstration of a prepared GROMACS molecular dynamics workflow.
• Analyzing molecular trajectories using RMSD, RMSF and radius of gyration (Rg).
• Evaluating hydrogen-bond patterns and protein–ligand interaction persistence.
• Integrating docking, ADMET and molecular dynamics findings for preliminary drug candidate assessment.

🛠️ Hands-On Capstone: GROMACS Simulation Analysis & Drug Candidate Prioritization

Analyze a precomputed GROMACS trajectory of an EGFR–inhibitor complex. Examine conformational stability, molecular fluctuations and intermolecular interactions. Combine the findings with Day 2 docking and ADMET results to prepare a short computational drug discovery report.

🧰 Tools: GROMACS, Python, Google Colab/Jupyter Notebook, PyMOL.

Who Should Enrol?

Important Dates

Workshop Outcomes

Fee Structure