Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Computer-Aided Drug Design has become an indispensable part of modern pharmaceutical research. By simulating molecular interactions, predicting ADMET properties, and screening millions of compounds virtually, CADD significantly speeds up early-stage drug discovery. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Computer-Aided Drug Design has become an indispensable part of modern pharmaceutical research. By simulating molecular interactions, predicting ADMET properties, and screening millions of compounds virtually, CADD significantly speeds up early-stage drug discovery.
📋 Course Objectives
1. Translate biotechnology theory into practical, reproducible analysis.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• Tangible, reproducible biotechnology work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Preparing a Target for Screening
• Binding site detection with fpocket and the risk of screening a crystallographic artefact
• Protonation states, tautomers and missing loops as the usual source of bad docking
• Holo versus apo structures and why induced fit breaks rigid-receptor assumptions
Compound Sets and Their Filters
• ZINC and ChEMBL as sources, and the difference between purchasable and virtual
• Lipinski, Veber and PAINS filters — what each removes and what each wrongly removes
• 3D conformer generation with RDKit and the conformer-count trade-off
Structure-Based Virtual Screening
• AutoDock Vina and Glide scoring functions and their weak correlation with affinity
• Redocking and cross-docking as the only honest validation of a docking protocol
• Enrichment metrics: ROC AUC and BEDROC against DUD-E style decoys
QSAR and ADMET Prediction
• Molecular descriptors and fingerprints (ECFP) versus learned graph representations
• Scaffold splits rather than random splits, or the model reports fantasy accuracy
• ADMET endpoints — hERG, CYP inhibition, solubility — and the applicability domain
Running a Campaign End to End
• Hit triage: consensus scoring, visual inspection and chemical common sense
• MM-GBSA rescoring and short MD to test pose stability before committing
• Documenting a screen so the result can be reproduced by someone else
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | AutoDock Vina |
| Covered Tool / Platform | PyRx |
| Covered Tool / Platform | Schrödinger Suite |
| Covered Tool / Platform | GROMACS |
| Covered Tool / Platform | ChemDraw |
| Covered Tool / Platform | Discovery Studio |
| Covered Tool / Platform | ADMET Predictor |
Frequently Asked Questions
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