Structure-Based In Silico Drug Design

Introduction to the Course

This advanced internship is designed for learners who want to understand how modern drug discovery is accelerated using computational tools—the same kind of workflows used in pharma, biotech, and research labs. Instead of only learning definitions, you’ll practice the full journey: identifying a biological target, retrieving and analyzing protein structures, designing or selecting ligands, and running molecular docking simulations to predict binding and interactions.
The program blends clear theory with real-world case studies and hands-on projects, so you don’t just “run software”—you learn how to think like a structure-based drug design researcher and interpret results with confidence.

Course Objectives

• Understand the principles of Structure-Based Drug Design (SBDD) and how it fits into the drug discovery pipeline.
• Learn to identify therapeutic targets and analyze 3D protein structures using trusted databases and tools.
• Design and prepare ligands using practical cheminformatics workflows.
• Perform molecular docking using AutoDock/AutoDock Vina and interpret binding interactions.
• Evaluate docking results and screen candidates for drug-likeness and ADMET properties.

What Will You Learn (Modules)

Module 1: Introduction to Drug Discovery

• Drug development pipeline: from target identification to lead optimization.
• Understanding structure-based vs ligand-based drug design.
• Types of drug targets: enzymes, receptors, DNA/RNA, and more.
• How computational screening supports faster and smarter decision-making.

Module 2: Protein Structure and Target Identification

• Understanding PDB files, protein domains, and what structure tells you about function.
• Tools you’ll work with: RCSB PDB, UniProt, SWISS-MODEL.
• Hands-on: retrieving a target structure and exploring active sites and binding regions.
• Learning how to choose a target that makes sense for docking-based studies.

Module 3: Ligand Design & Chemical Libraries

• Introduction to small molecules, ligands, and chemical space in drug discovery.
• Using ligand libraries and databases: PubChem, ZINC, ChemSpider.
• Drawing and editing ligands: MarvinSketch basics and ChemDraw fundamentals.
• Preparing ligands for docking (formats, geometry, and readiness checks).

Module 4: Molecular Docking Theory

• Docking algorithms explained clearly: rigid vs flexible docking.
• Docking parameters and scoring functions—what they mean and how they affect results.
• Introduction to AutoDock and AutoDock Vina and where each is useful.
• How to avoid common docking mistakes that lead to “good-looking but wrong” results.

Module 5: Docking Practical – Part I

• Preparing protein and ligand structures for a clean docking workflow.
• Setting up the grid box and configuration files correctly.
• Running docking simulations using AutoDock Vina.
• Understanding how to run repeatable docking experiments (not one-off trials).

Module 6: Docking Practical – Part II

• Analyzing docking results: binding affinities, poses, and interaction sites.
• Visualizing docking output using PyMOL / Discovery Studio.
• Interpreting interactions: hydrogen bonds, hydrophobic contacts, key residues.
• Learning what makes an interaction biologically meaningful—not just visually attractive.

Module 7: Drug-Likeness and ADMET Screening

• Understanding Lipinski’s Rule of Five and basic drug-like filtering.
• Tools you’ll use: SwissADME, pkCSM.
• Predicting toxicity, bioavailability, and basic ADMET risks early.
• How to use ADMET insights to improve shortlisting and reduce false leads.

Module 8: Case Study and Literature Discussion

• Case study example: docking an EGFR kinase inhibitor or HIV protease inhibitor.
• Paper discussion: recent trends in in silico drug discovery and what researchers are doing today.
• How to read docking/virtual screening papers critically and spot weak conclusions.

Final Project

For the final project, you’ll complete a mini structure-based drug design workflow. You’ll select a target and ligand set, run docking, interpret binding interactions, and support your shortlist with drug-likeness/ADMET screening—then present your work like a real computational project.

Deliverables include: target & ligand retrieval, docking output analysis, interaction visuals, ADMET screening summary, and a final report.

Who Should Take This Course?

This internship is ideal for:

• Postgraduate Students & PhD Scholars: Life Sciences, Pharmacy, Bioinformatics, or related fields.
• Research Interns & Academicians: Looking to add structure-based computational workflows to their research.
• Industry Professionals: Exploring computational drug discovery as a skill upgrade or career transition.

Job Opportunities

After completing this internship, you’ll be prepared for roles such as:

• Computational Drug Discovery Intern/Associate: Supporting docking and structure-based screening tasks.
• Molecular Docking Analyst: Running docking workflows and interaction-based shortlisting.
• Cheminformatics / Bioinformatics Research Assistant: Working with targets, ligands, and screening pipelines.
• SBDD Trainee: Supporting structure-guided design and early-stage lead selection.

Why Learn With Nanoschool?

At Nanoschool, we make complex drug discovery workflows feel practical, structured, and confidence-building.

• Hands-on Training: You won’t just learn docking—you’ll actually run the complete workflow.
• Industry-Relevant Tools: Learn platforms used widely in academic and early discovery pipelines.
• Case Study Learning: Real target examples help you connect tools to real research questions.
• Portfolio Outcome: Finish with a project report you can showcase for internships, research roles, or interviews.

Key outcomes of the course

• Understand and apply the fundamentals of Structure-Based Drug Design (SBDD).
• Retrieve and analyze protein targets using trusted structural biology resources.
• Prepare ligands and perform docking using AutoDock Vina.
• Visualize and interpret protein–ligand interactions with confidence.
• Screen candidates using drug-likeness and ADMET tools and present a final report.

FAQs

• What is structure-based drug design (SBDD)?
  SBDD uses 3D protein structures to guide the discovery and optimization of ligands that can bind to a target and potentially become drug candidates.
• Will I learn AutoDock and AutoDock Vina?
  Yes. You will learn how docking works, how to run docking using AutoDock Vina, and how to analyze and visualize the results.
• Do I need prior coding experience?
  No. This internship is tool-based and workflow-driven. You will focus on correct setup, analysis, and interpretation—not heavy programming.
• What will I submit for the final project?
  You’ll submit a complete mini-project report including target/ligand selection, docking results, interaction visuals, ADMET screening, and conclusions.
• Is ADMET screening included?
  Yes. You’ll learn how to check drug-likeness and predict basic ADMET properties using tools like SwissADME and pkCSM.