AI-Driven Drug Discovery: Tools, Workflows and Applications
Understanding what drug discovery is, why it is challenging, and how AI is helping researchers make the process faster, smarter, and more data-driven.
Skills you will gain:
About Program:
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.
Program Objectives:
- 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.
What you will learn?
📅 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
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- 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.
Career Supporting Skills
Program 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.