About this course
The Two Month: Molecular Docking and Drug Design Research Course at Dr. Omics Edu is a 2-month live, mentor-led online course in computer-aided drug design (CADD). It is research-oriented, so docking and drug design are applied to a question you can write up. You learn on real biological data and finish with a drug design analysis on a real target that you can show to an employer, a guide or an admissions panel.
In this course you move through target selection and structure preparation, ligand library design and molecular docking and scoring, then apply them in a hands-on project. The course runs live online across 2 modules, so you can ask your mentor questions as you work. It is an intermediate-level course, suited to learners who already know the basics.
What you will learn
Advanced molecular docking.
Docking pose evaluation and consensus scoring.
Compound library preparation and filtering.
Structure-based and ligand-based virtual screening.
Similarity searching and hit identification.
Pharmacophore modeling and pharmacophore-based screening.
QSAR fundamentals,QSAR model development and validation.
Lead identification, lead optimization and ADME/Tox assessment.
CADD scientist, computational chemist, drug discovery research associate, and a strong profile for pharma R&D or higher studies.
Skills you will gain
PyMOL
AutoDock Vina
PyRx
Discovery Studio Visualizer
SwissADME
ProTox
PubChem
ChEMBL
ZINC
PDB
UniProt
Pharmacophore modeling and virtual-screening tools RDKit for molecular descriptors
QSAR workflows
Course curriculum
2 modules
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T1 = Introduction to Drug Discovery Process
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T2 = Role of Computational Methods
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T3 = Hands-on: Chemical Structure Visualization
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T4 = Biomolecules and Their Properties
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T5 = Structure of Proteins and Ligands
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T6 = Hands-on: Protein Structure Visualization
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T7 = Molecular Visualization Tools
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T8 = Molecular Mechanics and Dynamics Simulations
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T9 = Molecular Mechanics and Dynamics Simulations (continued)
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T10 = Chemical Databases and Data Mining
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T11 = Ligand and Structure-Based Virtual Screening
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T12 = Hands-on: Chemical Data Exploration
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T13 = Advanced Virtual Screening Techniques
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T14 = Virtual Screening using Autodock Vina
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T15 = Principles of Molecular Docking
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T16 = Scoring Functions in Docking
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T17 = Hands-on: Molecular Docking
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T18 = Introduction to Molecular Dynamics
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T19 = Simulation Software (e.g., GROMACS)
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T20 = Hands-on: Analyzing MD Data
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T21 = Chemoinformatics: Data Analysis and Visualization
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T22 = Protein-Ligand Interaction Analysis
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T23 = Hands-on: Protein-Ligand Interaction Analysis
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T24 = Pharmacophore Modeling and Applications
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T25 = Chemoinformatics: Data Analysis and Visualization (continued)
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T26 = Structure-Based Drug Design
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T27 = Ligand-Based Drug Design
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T28 = Hands-on Structure-Based and Ligand-Based Drug Design
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T29 = ADMET in Drug Development
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T30 = Course Conclusion
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T1 = Basics of Machine Learning
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T2 = Supervised, Unsupervised, and Reinforcement Learning
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T3 = Hands-on: Learn the Basics with scikit-learn Library in Python
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T4 = Data Cleaning and Feature Selection
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T5 = Handling Molecular Data
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T6 = Hands-on: Use Pandas and NumPy for Data Preprocessing
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T7 = Regression and Classification Algorithms
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T8 = Deep Learning in Drug Discovery
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T9 = Hands-on: Implement Machine Learning Models using scikit-learn
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T10 = Hands-on: Implement Machine Learning Models using TensorFlow/Keras
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T11 = Predicting Drug-Target Interactions
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T12 = QSAR Modeling
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T13 = Hands-on: Apply Machine Learning to Real Datasets with RDKit
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T14 = Hands-on: Apply Machine Learning to Real Datasets with Cheminformatics
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T15 = Structure-Activity Relationship (SAR) Analysis
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T16 = Hands-on: Use RDKit for SAR Analysis
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T17 = De Novo Drug Design using ML
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T18 = Explore De Novo Design Tools
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T19 = Advanced Machine Learning Techniques in Drug Design
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T20 = Integration of Omics Data in Drug Discovery
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T21 = Clinical Trial Design and Data Analysis
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T22 = Ethical Considerations in Drug Design and Machine Learning
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T23 = Real-World Applications
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T24 = Q&A and Discussion
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T25 = Conclusion
What you need to start
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Basic knowledge of Biology or Life Sciences
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Fundamental understanding of proteins and DNA
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Familiarity with computers and internet usage
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Interest in drug discovery and pharmaceutical research
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No prior CADD experience is required
Who this course is for
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B.Pharm, M.Pharm, B.Sc, M.Sc, B.Tech, M.Tech and PhD students in pharmacy, chemistry, biotechnology or life sciences.
