Computational Modeling of Targeted Protein Degradation (TPD): AI-Assisted Design of PROTACs and Molecular Glues
Accelerating PROTAC and Molecular Glue Discovery through AI and Computational Modeling
About This Course
Targeted Protein Degradation (TPD) is an emerging therapeutic strategy that eliminates disease-causing proteins by directing them to the cell’s natural protein degradation machinery. Technologies such as PROTACs (Proteolysis Targeting Chimeras) and Molecular Glues have transformed modern drug discovery by enabling the selective degradation of previously “undruggable” proteins. Artificial Intelligence (AI), structural bioinformatics, and molecular modeling are increasingly used to accelerate the identification, design, and optimization of these novel therapeutic molecules.
This 3-day international workshop introduces participants to the fundamentals of Targeted Protein Degradation and demonstrates how computational tools, structural databases, molecular visualization, and AI-assisted workflows can support the design and evaluation of PROTACs and Molecular Glues. Through practical hands-on sessions using Google Colab, Protein Data Bank (PDB), molecular visualization software, and AI-based analysis tools, participants will gain practical experience in computational TPD research.
Aim
To provide participants with a practical understanding of Targeted Protein Degradation (TPD) and demonstrate how AI, structural bioinformatics, and computational modeling can support the design, analysis, and optimization of PROTACs and Molecular Glues for next-generation drug discovery.
Workshop Objectives
- Understand the principles of Targeted Protein Degradation and its role in modern drug discovery.
- Learn the mechanisms of PROTACs and Molecular Glues.
- Explore protein structures and ligand interactions using public structural databases.
- Understand computational approaches for protein–ligand interaction analysis.
- Learn how AI can support PROTAC design and candidate prioritization.
- Explore current research trends and future applications of AI in targeted protein degradation.
Workshop Structure
📅 Day 1: Foundations of Targeted Protein Degradation and Structural Bioinformatics
- Introduction to Targeted Protein Degradation (TPD)
- Understanding PROTACs and Molecular Glues
- Protein degradation pathways and the ubiquitin–proteasome system
- Components of a PROTAC: Target protein, linker, and E3 ligase
- Introduction to structural bioinformatics in drug discovery
- Exploring Protein Data Bank (PDB) structures relevant to TPD
- Current trends in AI-assisted targeted protein degradation
🛠️ Hands-on Activity:
Exploring Protein Structures for Targeted Protein Degradation
Participants will use Protein Data Bank (PDB) and Mol* or PyMOL to visualize target proteins and E3 ligases involved in targeted protein degradation. They will explore protein structures, identify ligand-binding regions, and understand the structural basis of PROTAC-mediated protein degradation.
📅 Day 2: Computational Modeling of Protein–Ligand Interactions
- Fundamentals of protein–ligand interactions
- Ternary complex formation in PROTACs
- Molecular docking concepts for TPD
- Introduction to protein interaction prediction
- Computational evaluation of binding affinity
- AI-assisted prediction of protein–ligand interactions
- Basics of molecular interaction visualization
🛠️ Hands-on Activity:
Visualizing Protein–Ligand Interactions and Ternary Complexes
Participants will use Google Colab and publicly available structural datasets to visualize protein–ligand interactions, explore simplified docking results, analyze binding interfaces, and investigate the formation of ternary complexes involved in targeted protein degradation.
📅 Day 3: AI-Assisted PROTAC Design and Candidate Prioritization
- AI in targeted protein degradation research
- Virtual screening of PROTAC candidates
- Molecular descriptor analysis
- Ranking candidate PROTACs using computational properties
- Explainable AI (XAI) for drug discovery
- Generative AI and Large Language Models (LLMs) in medicinal chemistry
- Future trends: AI-driven drug design, digital medicinal chemistry, autonomous molecular discovery, and precision therapeutics
🛠️ Hands-on Activity:
AI-Based Screening and Ranking of PROTAC Candidates
Participants will use Google Colab to analyze a curated dataset of PROTAC-like molecules, calculate molecular descriptors, compare candidate properties, and build a simple machine learning model to rank compounds based on predicted binding potential, drug-likeness, and physicochemical properties for targeted protein degradation applications.
Who Should Enrol?
This workshop is designed for undergraduate and postgraduate students, Ph.D. scholars and research fellows, faculty members and academicians, bioinformatics and computational biology researchers, structural biologists, medicinal chemists, drug discovery scientists, pharmaceutical and biotechnology professionals, AI and machine learning researchers, and industry professionals working in computational drug discovery, structural biology, and precision therapeutics.
Important Dates
Registration Ends
October 1, 2026
IST 4:30 pm
Workshop Dates
October 1, 2026 – October 3, 2026
IST 5:00 pm
Workshop Outcomes
- Understand the principles and applications of Targeted Protein Degradation.
- Explain the mechanisms of PROTACs and Molecular Glues in drug discovery.
- Explore protein structures and ligand interactions using structural bioinformatics tools.
- Interpret protein–ligand interaction data and ternary complex formation.
- Apply AI-assisted approaches for computational screening and prioritization of PROTAC candidates.
- Gain practical experience with molecular visualization, structural databases, and computational drug discovery workflows.
- Explore emerging trends in AI-driven targeted protein degradation and precision therapeutics.
Fee Structure
Student Fee
₹1999 | $65
Ph.D. Scholar / Researcher Fee
₹2999 | $75
Academician / Faculty Fee
₹3999 | $85
Industry Professional Fee
₹4999 | $100
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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