About this internship
The 3 Month Computer Aided Drug Design Internship with Live Projects at Dr. Omics Edu is a 3-month live, mentor-supervised internship in computer-aided drug design (CADD) under NEP 2020. It centres on live CADD projects so that docking and screening skills are applied to a real target. You work on a real dataset and a defined research question, and you finish with a docking or virtual screening project on a real drug target that you can show to an admissions panel, a guide or an employer.
In this internship you move through drug discovery pipeline and database resources, protein and ligand preparation and molecular docking with interaction analysis, then apply them in your own project. The programme runs live online with 3 modules, so you can ask your mentor questions as you work. It is open to learners at every level, and mentors adjust the pace to your background.
What you will achieve
Drug discovery pipeline and database resources
Protein and ligand preparation
Molecular docking with interaction analysis
Pharmacophore or ligand-based screening
ADMET prediction and lead prioritisation
A live CADD project with mentor review
For careers: Computational chemist, CADD scientist, drug discovery research associate, cheminformatics analyst, and a stronger profile for pharma R&D roles or higher studies.
The project work
Hands-on implementation of everything covered in the sessions
Industry-standard tools and workflows
Project documentation and a final presentation
Code review and optimisation sessions with your mentor
Skills you will gain
PDB
PubChem
AutoDock Vina
PyMOL
Discovery Studio Visualizer
SwissADME
GROMACS
Certification
Available
Issued by Dr. Omics Edu
Internship curriculum
3 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
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Understanding of Biochemistry fundamentals
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Basic Computer skills
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Interest in Drug Discovery
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No prior CADD experience required
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Suitable for beginners and advanced learners
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Passion for research and computational biology
Who this internship 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, biochemistry or life sciences.
