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Basics of Computational Drug Design and Molecular Docking
Rating: 3.1 out of 5(3 ratings)
13 students

Basics of Computational Drug Design and Molecular Docking

Structure-Based Drug Design: Docking, Molecular Dynamics, Free Energy Calculations, ADMET
Created bySinjini Bala
Last updated 12/2025
English

What you'll learn

  • Explain the core principles of computational drug design and molecular docking, including molecular recognition and structure-based drug discovery concepts.
  • Prepare proteins and ligands and perform molecular docking and virtual screening using standard computational workflows and tools.
  • Interpret docking, molecular dynamics, and free energy calculation results to evaluate ligand–receptor stability and binding affinity.
  • Assess drug-likeness, ADMET properties, and toxicity risks to support informed lead selection and optimization in drug discovery.

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Course content

2 sections • 12 lectures • 1h 47m total length

  • Introduction to Computational Drug Design8:58

    1.1 Evolution of drug discovery approaches

    1.2 Role and advantages of computational methods

    1.3 Key concepts: ligands, receptors, targets, binding sites

    1.4 Overview of in silico workflow in modern drug development

  • Fundamentals of Molecular Recognition9:11

    2.1 Chemical interactions: hydrogen bonds, hydrophobic forces, electrostatics

    2.2 Receptor–ligand complementarity

    2.3 ADME considerations in early design

    2.4 Importance of three-dimensional structure

  • Protein Structure and Function Essentials8:20

    3.1 Primary to quaternary structure

    3.2 Active sites, allosteric sites, and binding pockets

    3.3 Protein structural databases (PDB, UniProt)

    3.4 Structure preparation and cleaning for simulations

  • Ligand Chemistry and Small-Molecule Representation9:11

    4.1 Chemical structure formats (SMILES, SDF, MOL2)

    4.2 Tautomers, stereochemistry, and protonation states

    4.3 Ligand libraries and compound databases (PubChem, ChEMBL, ZINC)

    4.4 Ligand preprocessing and energy minimization

  • Virtual Screening Methods8:46

    5.1 High-throughput virtual screening

    5.2 Ligand-based vs. structure-based screening

    5.3 Pharmacophore modeling and applications

    5.4 Hit identification and prioritization

  • Principles of Molecular Docking9:44

    6.1 Rigid vs. flexible docking

    6.2 Scoring functions: types and limitations

    6.3 Docking algorithms: genetic algorithms, Monte Carlo, simulated annealing

    6.4 Preparing receptor and ligand files for docking

  • Docking Workflow Using Popular Tools9:22

    7.1 AutoDock and AutoDock Vina workflows

    7.2 Glide, GOLD, and MOE docking modules

    7.3 Selecting grid parameters and docking settings

    7.4 Evaluating docking accuracy (RMSD, scoring consistency)

  • Interpreting Docking Results9:02

    8.1 Analyzing binding poses

    8.2 Understanding interaction fingerprints

    8.3 Visualizing results using PyMOL, Chimera, and Discovery Studio

    8.4 Selecting top hits for further study

  • Molecular Dynamics (MD) Basics8:57

    9.1 Introduction to MD simulations

    9.2 Force fields (CHARMM, AMBER, GROMOS)

    9.3 System setup, minimization, and equilibration

    9.4 MD for refining docking results

  • Free Energy Calculations8:56

    10.1 Binding free energy concepts

    10.2 MM-PBSA and MM-GBSA methods

    10.3 Alchemical free energy methods

    10.4 Assessing ligand–receptor stability and affinity

  • ADMET and In Silico Toxicity Prediction8:30

    11.1 Physicochemical and pharmacokinetic descriptors

    11.2 In silico toxicity models

    11.3 Tools for ADMET prediction (SwissADME, pkCSM)

    11.4 Integrating ADMET into computational workflows

  • Case Studies and Practical Applications8:14

    12.1 Structure-based drug design for infectious diseases

    12.2 Cancer target docking example

    12.3 Lead optimization using computational tools

    12.4 Translating in silico outcomes to experimental validation

Requirements

  • This course is designed to be accessible to beginners, and no prior experience in computational drug design or molecular modeling is required. Learners with a basic understanding of biology or chemistry at the undergraduate level will find the concepts easier to grasp. Familiarity with proteins, enzymes, and small molecules can be helpful, though all essential ideas are explained clearly from the fundamentals.

Description

Basics of Computational Drug Design and Molecular Docking is a comprehensive introductory course designed to provide learners with a strong foundation in modern in silico drug discovery approaches. As drug development increasingly relies on computational tools to reduce time, cost, and experimental failure, this course equips learners with the conceptual understanding needed to navigate structure-based drug design workflows with confidence.

The course begins by introducing the fundamental principles of molecular recognition, protein structure, and ligand chemistry, establishing the biological and chemical basis of drug–target interactions. Learners are guided through essential computational techniques such as virtual screening and molecular docking, with emphasis on understanding docking algorithms, scoring functions, and result interpretation rather than software-specific complexity. The course then advances into molecular dynamics simulations, where learners explore protein and ligand flexibility, system stability, and time-dependent behavior of biomolecular complexes.

Further modules focus on binding free energy calculations and ADMET prediction, helping learners understand how computational methods assess binding strength, drug-likeness, pharmacokinetics, and toxicity risks. Real-world case studies in infectious diseases and cancer drug discovery illustrate how these tools are applied in practical research and pharmaceutical pipelines. Throughout the course, emphasis is placed on integrating multiple computational techniques to support rational decision-making in drug discovery.

This course is designed for beginners and early-career learners from life sciences, biotechnology, pharmacy, chemistry, and related fields. By the end of the course, learners will have a clear, structured understanding of how computational drug design supports experimental research and contributes to the development of safer, more effective therapeutic candidates.

Who this course is for:

  • This course is intended for students and early-career learners who want a clear and structured introduction to computational drug design and molecular docking. It is especially valuable for learners from life sciences, biotechnology, pharmacy, chemistry, biomedical sciences, and bioinformatics who wish to understand how computational tools are used in modern drug discovery. The course is also well suited for beginners with no prior experience in in silico methods, as all concepts are introduced from the basics and explained in a step-by-step manner. Additionally, researchers, educators, and professionals who want to build foundational knowledge or transition into computational and structure-based drug design will find this course useful for strengthening their conceptual understanding and practical decision-making skills.

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