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AI in Drug Discovery – Beginner’s Hands-on Workshop
AI in Drug Discovery: AI/ML, Molecular Prediction, Docking, ADMET & Lead Prioritisation
- PRAC-G Bioinformatics Lab
- Batch 25 Sep, 2026–27 Sep, 2026
- Bhubaneswar
Key dates
Enrolment opens31 Aug, 2026
Enrol by24 Sep, 2026
Batch starts25 Sep, 2026
Batch ends27 Sep, 2026
Course
- FormatOnline
Participation
- Price₹799
- Seats25 seats
Key dates
Enrolment opens31 Aug, 2026
Enrol by24 Sep, 2026
Batch starts25 Sep, 2026
Batch ends27 Sep, 2026
Course
- FormatOnline
Participation
- Price₹799
- Seats25 seats
About this course
AI in Drug Discovery: AI/ML, Molecular Prediction, Docking, ADMET & Lead Prioritisation
AI in Drug Discovery — Beginner Hands-on Workshop
A beginner-level workshop introducing the role of Artificial Intelligence (AI) and Machine Learning (ML) in modern drug discovery through a practical, step-by-step approach.
Students will move from understanding a biological target and chemical data to AI-based prediction, molecular docking, ADMET/drug-likeness assessment, and final lead prioritisation.
The workshop covers the major stages of the drug-discovery pipeline, AI/ML concepts, biological and chemical databases, molecular data representation, molecular activity prediction, protein structure analysis, molecular docking, ADMET and drug-likeness, and AI-assisted lead selection.
Duration: Duration: 3 Days | Dates: 25–27 September 2026 | Time: 8:00 PM–10:00 PM IST
Format: Lecture + Demonstration + Guided Hands-on Practical
What you'll learn
- Drug Discovery Fundamentals
- Artificial Intelligence & Machine Learning in Drug Discovery
- Drug Discovery Databases — PubChem, ChEMBL, RCSB PDB & UniProt
- Molecular Data & Molecular Representation
- Machine Learning Basics
- Molecular Descriptors & Fingerprints
- AI-Based Molecular Activity Prediction
- Protein-Ligand Interaction
- Molecular Docking
- ADMET & Drug-Likeness
- AI-Assisted Lead Prioritisation
Topics covered
Drug DiscoveryAI & MLMolecular DataMolecular PredictionProtein StructureMolecular DockingADMETDrug-LikenessLead Prioritisation
Instruments used
PubChemChEMBLUniProtRCSB PDBRDKitPythonGoogle Colab/Jupyterscikit-learnAlphaFold DBAutoDock VinaPyMOL
Curriculum
What the course covers
A structured, module-by-module breakdown of the course journey.
Module 1
Drug Discovery Fundamentals
- Introduction to Drug Discovery
- Where AI/ML Fits into Drug Discovery
- Drug Discovery Pipeline & AI Applications
- Exploring Drug Discovery Databases
Module 2
AI & Machine Learning Basics
- What are AI, Machine Learning & Deep Learning
- Machine Learning Basics for Drug Discovery
- Training Data, Features, Labels & Prediction
- Introduction to ML Models
Module 3
Molecular Data & AI Prediction
- How AI Understands a Molecule
- SMILES, Molecular Descriptors & Fingerprints
- Molecular Activity Prediction
- Hands-on AI-Based Prediction
Module 4
Protein Structure & Molecular Docking
- Protein Structure in Drug Discovery
- AlphaFold & Predicted Protein Structures
- Protein–Ligand Interactions
- Molecular Docking with AutoDock Vina
Module 5
ADMET & Drug-Likeness
- Introduction to Drug-Likeness
- Lipinski’s Rule of Five
- ADMET Analysis
- Interpreting ADMET Predictions
Module 6
AI-Assisted Lead Prioritisation
- Integrating AI Prediction, Docking & ADMET
- Comparing Candidate Molecules
- AI-Assisted Lead Selection
- Final Lead Prioritisation & Scientific Rationale
Instructor
Who teaches this course
Surbhi ma'am
Computational Biologist & Chemist working at Karyon Bio
Requirements
What you'll need
Everything you should bring to get the most out of this course.
Prerequisites
What you should already know
- Basic understanding of biology
- Basic knowledge of molecular biology
- No prior AI/ML experience required
Recommended knowledge
Helpful, not required
- Basic understanding of proteins and molecules
- Familiarity with biological databases
- Basic computer skills
Software requirements
Tools you'll use in the course
- Google Colab / Jupyter Notebook
- Python
- RDKit
- scikit-learn
- AutoDock Vina
- PyMOL
Hardware requirements
Equipment you'll need
- Laptop/Desktop
- Stable internet connection
- Minimum 4 GB RAM recommended
Syllabus & resources
Inside the course
Frequently asked questions
Want to know more? Talk to us
PracG
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