AI-Driven Drug Discovery: Tools, Workflows and Applications is a 2-day online mentor-led workshop designed to introduce UG and PG students to the growing role of Artificial Intelligence in drug discovery and pharmaceutical research. The workshop covers the fundamentals of drug discovery, biomedical and molecular data, AI-based virtual screening, drug-likeness prediction, ADMET analysis, lead optimization, and future applications of AI in biomedical innovation.
Participants will explore key databases and tools such as PubChem, ChEMBL, DrugBank, Protein Data Bank, UniProt, BindingDB, SwissADME, Molinspiration, and Google Colab. Through guided activities, students will map AI applications across the drug discovery pipeline and evaluate sample compounds based on drug-like properties and basic screening parameters.
By the end of the workshop, participants will gain a clear understanding of how AI is transforming drug discovery and how computational tools can support faster, smarter, and more efficient pharmaceutical research.
Aim
To introduce students to the basic concepts, applications, tools, databases, and workflows of Artificial Intelligence in drug discovery and pharmaceutical research. The workshop will help participants understand how AI supports target identification, virtual screening, drug-likeness prediction, ADMET analysis, lead optimization, and future biomedical innovation.
What Participants Will Learn
To introduce participants to the fundamentals of drug discovery and the role of AI in pharmaceutical research.
To help students understand how biological, chemical, and molecular data are used in AI-driven drug discovery.
To familiarize participants with important databases and tools such as PubChem, ChEMBL, DrugBank, PDB, UniProt, SwissADME, and Molinspiration.
To explain key concepts such as virtual screening, drug-likeness, ADMET prediction, and lead optimization.
To provide students with a beginner-friendly understanding of how AI can support faster and smarter drug candidate identification.
Structure
📅 Day 1: Introduction to Drug Discovery and Biomedical Data
Basics of drug discovery and drug development
Traditional drug discovery pipeline
Target identification, hit discovery, and lead optimization overview
Role of AI, Machine Learning, and Deep Learning in pharmaceutical research
Introduction to genes, proteins, receptors, enzymes, compounds, ligands, and small molecules
Overview of key databases: PubChem, ChEMBL, DrugBank, PDB, UniProt, BindingDB, and ClinicalTrials.gov
Activity: Drug Discovery Pipeline Mapping Activity
📅 Day 2: AI-Based Screening, Drug-Likeness, and ADMET
AI-based virtual screening and compound selection
Molecular properties and drug candidate evaluation
Drug-likeness and Lipinski’s Rule of Five
ADMET prediction: absorption, distribution, metabolism, excretion, and toxicity
Solubility, bioavailability, toxicity, and safety prediction
Lead optimization and molecular docking concept overview
AI limitations, ethics, explainability, and future career pathways
Activity: Drug Candidate Selection and Mini AI Workflow Activity
Important Dates
Registration Ends
5.30 PM
Workshop Dates
2026-05-09
6.30 PM
6.30 PM
What You Will Gain
e-Certificate of Completion from NSTC/NanoSchool
e-Marksheet / Performance Record (if applicable)
Access to Recorded Sessions (for revision and learning support)
List of Curated Databases and Tools (PubChem, ChEMBL, DrugBank, SwissADME, etc.)
Basic Learning Resources for AI & Drug Discovery
Mentor Support during Sessions
Participation in Interactive Activities and Q&A Sessions
Outcomes
Understand the basic drug discovery pipeline and its major stages.
Explain how AI, Machine Learning, and Deep Learning are applied in drug discovery.
Identify common biological and chemical data used in pharmaceutical research.
Explore important drug discovery databases and online tools.
Understand drug-likeness, Lipinski’s Rule of Five, ADMET prediction, and lead optimization.
Design a simple AI-based workflow for identifying and evaluating potential drug candidates.
Who Should Attend
UG and PG students from Biotechnology, Pharmacy, Life Sciences, Microbiology, Biochemistry, Bioinformatics, Computational Biology, and Pharmaceutical Sciences.
Students interested in drug discovery, pharmaceutical research, bioinformatics, AI, and computational biology.
Beginners who want to understand how Artificial Intelligence is used in biomedical and pharmaceutical research.
Research scholars and early-stage learners planning to work in AI-driven drug discovery, molecular biology, or pharmaceutical data analysis.
Deliverables
e-Certificate of Completion from NSTC/NanoSchool
e-Marksheet / Performance Record (if applicable)
Access to Recorded Sessions (for revision and learning support)
List of Curated Databases and Tools (PubChem, ChEMBL, DrugBank, SwissADME, etc.)
Basic Learning Resources for AI & Drug Discovery
Mentor Support during Sessions
Participation in Interactive Activities and Q&A Sessions
roopaSC
ANALYST
Speciality: Bioinformatics Analyst, Drug Discovery Research Associate, Molecular Modeling Trainee
