Computational Drug Discovery and Design


About This Course

Computational Drug Designing has become the go-to requirement for the researchers, scientists and the pharmaceuticals who fight against the fatal disease.

Allowing the research for the drug discovery, designing and the development to be done at an exponential speed as compared to the traditional drug development approaches.

Keeping the demand of the scientific community in-check and the free time most of the community has these days due to COVID-19. BioCode (in partners with BioAfri) is presenting an opportunity to learn Computational Drug Discovery and Design through an online workshop.

All the modules are carefully crafted so you learn from the basics of the Bioinformatics: Computational Drug Discovery and Design to the advanced level. Efficient demonstrations of the tools, methodologies, algorithms and software suites that are utilized within Computational Drug Designing will help you learn how to start the Computational Drug Designing projects, identify leads, do Molecular Docking, predict and analyze the ADMET properties, predict 3D structures of proteins and much more.

Learning Objectives

Bioinformatics: Role in Drug Design
Lead Identification
ADMET Properties
Protein 3D Structure Prediction
Molecular Docking: A Powerful Tool for Structure Based Drug Design
Molecular Docking Using Autodock 4.2
PyMol and PyRx
Discovery Studio+
Reverse (Computational) Vaccinology & Immunoinformatics
Drug Repurposing


  • You don't need any prior Bioinformatics, programming or immunoinformatic knowledge or skills for this course, we'll take care of that and teach you the required topics!
  • Basic immunology background is a plus point, however, if you lack this knowledge, during the course, you'll be taught the basics as well.
  • This course is for both beginners and professionals alike, anyone with biology background can avail this course.

Target Audience

  • The target audience for this Computational Drug Discovery and Design workshop are biologists, beginner or intermediate Bioinformaticians or data analysts with no or little experience in applications of computational bioinformatics and bioinformatics pipelines for protein analysis.
  • However, a superficial understanding of molecular biology and immunology is expected from you before you join the course.
  • This hands-on guide will help you properly to understand Immunoinformatic and Bioinformatics, and it is quite easy to get started in, even if you lack a proper understanding of the underlying concepts of Bioinformatics databases, servers, tools and the algorithms working behind them.


25 Lessons

Module I: Bioinformatics: Role in Drug Design

1. Introduction /history/coverage 2. Application in drug design 3. Steps involved in computer aided drug/Vaccine design 4. Various virtual screening studies (Ligand based drug design/Structure based drug design) 5. Importance of Molecular docking in drug design
Practical Workflows8:57

Module II: Lead Identification

1. Literature review 2. Various Databases for Lead Identification 3. Identification of Target disease and enzyme 4. Various protein data banks 5. Steps involved

Module III: ADMET Properties

1. Drug Likeness/A/DMET properties/Lipinski’s rule of five 2. Various websites/software’s of A/dmet Properties 3. Steps involved 4. Predicting ADMET properties 5. Result interpretation

Module IV: Protein 3D Structure Prediction

1. Introduction and Reasoning 2. Types of 3D Structure Prediction 3. Most cited 3D Structure Prediction Tools 4. Homology Modeling - Find the Relatives 5. Threading - String Them Together 6. Ab inito - The Guessing Game 7. Model Visualization 8. Model Evaluation & Comparative Analysis of the Results 9. Conclusions 10. Leading Path

Module V: Molecular Docking: A Powerful Tool for Structure Based Drug Design

1. Introduction 2. Various Docking Methodologies 3. Algorithms and scoring functions 4. Various docking websites (Free software’s/Commercial software’s) 5. Applications necessary for docking 6. Setup of docking software

Module VI: Molecular Docking Using Autodock 4.2

1. Steps involved in molecular docking (Energy optimization, downloads from the databases, preparation etc.) 2. Binding interactions, screening and their evaluation 3. Observations & Result analysis 4. Molecular dynamics

Module VII: PyMol and PyRx

1. Steps involved, 2. Derive results from PyMol, 3. Steps involved, 4. Result analysis, 5. Steps involved, 6. Derive results from PyRx, 7. Steps involved, 8. Result analysis.

Module VIII: Discovery Studio+

1. Steps involved, 2. Result analysis.

Module IX: Reverse (Computational) Vaccinology & Immunoinformatics

1. Introduction to Vaccinology, 2. Differences between Conventional & Computational Vaccinology, 3. Steps involved & Tools used, 4. Result Analysis.

Module X: Drug Repurposing

1. Introduction, 2. Steps involved, 3. Results analysis.


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All Levels
25 lectures

Material Includes

  • Step-by-step pre-recorded lectures
  • Learn any time, any where!
  • Transcription
  • Notes
  • BioPresentations
  • Exercises
  • Automated Evaluations (MCQs)
  • 100% Authentic Certificate

Enrolment validity: 365 days

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