About this course
The Four Month: Pharmacophore and QSAR Drug Design Skill Course at Dr. Omics Edu is a 4-month live, mentor-led online course in computer-aided drug design (CADD). It focuses on ligand-based design, where pharmacophore and QSAR models guide compound selection. 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 ligand-based drug design principles, pharmacophore model generation and pharmacophore-based screening, then apply them in a hands-on project. The course runs live online across 7 modules, so you can ask your mentor questions as you work. It is an advanced-level course, best suited to learners who already know the fundamentals.
What you will learn
• Ligand-based drug design principles
• Pharmacophore model generation
• Pharmacophore-based screening
• Building and validating QSAR models
• Applying models to design new compounds
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
7 modules
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M1T1 = Introduction to Bioinformatics
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M1T2 = NCBI Database Overview
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M1T3 = Genbank Database Practical Exercises
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M1T4 = UCSC Genome Browser Overview
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M1T5 = UCSC Genome Browser Hands-on Exercises
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M1T6 = Pubmed Database Introduction
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M1T7 = Clinvar Database Overview
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M1T8 = KEGG Database Overview and Exercises
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M1T9 = Protein Databases (UniProt)
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M1T10 = Protein Databases (PDB)
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M1T11 = Online BLAST Introduction and Exercises
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M1T12 = Standalone BLAST Setup and Exercises
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M1T13 = Standalone BLAST Advanced Exercises
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M1T14 = Multiple Sequence Alignment with ClustalW
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M1T15 = Multiple Sequence Alignment with MEGA
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M2T1 = Overview and Installation of Linux
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M2T2 = Basic Linux Commands
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M2T3 = Advanced Linux Commands
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M2T4 = Package Management using Repository
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M2T5 = Package Management using Source Code
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M3T1 = Introduction to Python
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M3T2= Data Types
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M3T3= String Handling
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M3T4= Data Structure
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M3T5=Control Structure
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M3T6 = Function
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M3T7= File Handling
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M3T8= Data Manipulation
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M3T9= Data Visualization
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M3T10= Biopython
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M4T1 = Introduction and Installation of R
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M4T2= Data Types in R
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M4T3= Data Structure
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M4T4= File Handling
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M4T5=Control Structure
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M4T6 = Function
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M4T7= Package Management
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M4T8= Data Manipulation
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M4T9= Data Visualization
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M4T10= Statistical Analysis
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M6T1 =Introduction to Drug Discovery Process
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M6T2=Role of Computational Methods
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M6T3= Hands-on: Chemical Structure Visualization
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M6T4= Biomolecules and Their Properties
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M6T5= Structure of Proteins and Ligands
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M6T6= Hands-on: Protein Structure Visualization
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M7T7= Molecular Visualization Tools
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M6T8= Molecular Mechanics and Dynamics Simulations
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M6T9= Molecular Mechanics and Dynamics Simulations (continued)
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M6T10= Chemical Databases and Data Mining
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M6T11= Ligand and Structure-Based Virtual Screening
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M6T12= Hands-on: Chemical Data Exploration
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M6T13= Advanced Virtual Screening Techniques
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M6T14= Virtual Screening using Autodock Vina
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M6T15= Principles of Molecular Docking
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M6T16= Scoring Functions in Docking
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M6T17= Hands-on: Molecular Docking
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M6T18= Introduction to Molecular Dynamics
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M6T19= Simulation Software (e.g., GROMACS)
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M6T20= Hands-on: Analyzing MD Data
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M6T21= Chemoinformatics: Data Analysis and Visualization
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M6T22= Protein-Ligand Interaction Analysis
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M6T23= Hands-on Protein-Ligand Interaction Analysis
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M6T24= Pharmacophore Modeling and Applications
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M6T25= Chemoinformatics: Data Analysis and Visualization (continued)
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M6T26= Structure-Based Drug Design
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M6T27= Ligand-Based Drug Design
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M6T28= Hands-on Structure-Based and Ligand-Based Drug Design
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M6T29= ADMET in Drug Development
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M6T30= Course Conclusion
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M7T1= Basics of Machine Learning
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M7T2= Supervised, Unsupervised, and Reinforcement Learning
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M7T3= Hands-on: Learn the Basics with scikit-learn Library in Python
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M7T4= Data Cleaning and Feature Selection
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M7T5= Handling Molecular Data
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M7T6= Hands-on: Use Pandas and NumPy for Data Preprocessing
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M7T7= Regression and Classification Algorithms
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M7T8= Deep Learning in Drug Discovery
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M7T9= Hands-on: Implement Machine Learning Models using scikit-learn
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M7T10= Hands-on: Implement Machine Learning Models using TensorFlow/Keras
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M7T11= Predicting Drug-Target Interactions
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M7T12= QSAR Modeling
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M7T13= Hands-on: Apply Machine Learning to Real Datasets with RDKit
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M7T14= Hands-on: Apply Machine Learning to Real Datasets with Cheminformatics
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M7T15= Structure-Activity Relationship (SAR) Analysis
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M7T16= Hands-on: Use RDKit for SAR Analysis
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M7T17= De Novo Drug Design using ML
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M7T18= Explore De Novo Design Tools
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M7T19= Advanced Machine Learning Techniques in Drug Design
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M7T20= Integration of Omics Data in Drug Discovery
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M7T21= Clinical Trial Design and Data Analysis
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M7T22= Ethical Considerations in Drug Design and Machine Learning
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M7T23= Real-World Applications
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M7T24= Q&A and discussion
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M7T25= Conclusion
What you need to start
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Basic understanding of biology or life sciences
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Fundamental knowledge of biochemistry is beneficial
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Interest in computational biology and drug discovery
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Basic computer skills
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No prior CADD experience required
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Programming knowledge is optional but helpful
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.
