AI-Driven Drug Repurposing: Network Pharmacology, Biomedical Knowledge Graphs & Predictive Drug Discovery
From Drug–Target Interactions to Therapeutic Candidate Prioritization through Network Pharmacology, Graph Machine Learning & Virtual Live-Lab Experimentation
About This Course
This three-day, hands-on workshop explores how network pharmacology, biomedical knowledge graphs, and machine learning can be applied to investigate new therapeutic applications for existing drugs.
Participants will progress from disease-associated gene identification and drug–target interaction analysis to biological pathway enrichment, biomedical knowledge graph construction, and graph-based drug–disease prediction. The workshop introduces established research tools, including Open Targets, ChEMBL, STRING, Cytoscape, Reactome, and PyKEEN, to investigate molecular relationships and identify potential therapeutic candidates.
The workshop also includes practical training with the Repurpose Drug Discovery Lab at Live-Labs.org, where participants will explore biomedical knowledge graphs, examine drug–disease associations, evaluate supporting evidence, prioritize potential drug candidates, and develop testable therapeutic hypotheses.
The program connects computational drug discovery with applications in pharmacology, bioinformatics, systems biology, precision medicine, and pharmaceutical research.
Aim
To develop practical skills in network pharmacology, biomedical knowledge graph analysis, and AI-assisted drug repurposing, enabling participants to investigate drug–disease relationships, prioritize potential therapeutic candidates, and develop scientifically supported hypotheses for further research.
Workshop Objectives
Participants will learn to:
- Identify disease-associated genes and potential therapeutic targets.
- Retrieve and interpret drug–target interactions and bioactivity information.
- Analyze protein interaction networks and disease-associated pathways.
- Construct and interpret biomedical knowledge graphs.
- Understand graph embeddings and drug–disease link prediction.
- Apply computational approaches to therapeutic candidate prioritization.
- Evaluate drug safety signals and supporting biomedical evidence.
- Compare graph-based prediction results and candidate rankings.
- Develop evidence-based, testable drug repurposing hypotheses.
Workshop Structure
📅 Day 1 – Network Pharmacology, Drug–Target Identification & Disease Pathway Analysis
- Introduction to drug repurposing and network pharmacology in computational drug discovery.
- Identification of disease-associated genes and therapeutic targets using Open Targets.
- Drug–target interactions, mechanisms of action, and bioactivity analysis using ChEMBL.
- Drug and compound information retrieval using PubChem.
- Protein–protein interaction network construction using STRING.
- Molecular network visualization and identification of highly connected proteins using Cytoscape.
- Biological pathway enrichment and functional interpretation using Reactome.
- Integration of drug–gene–disease relationships for therapeutic hypothesis development.
🛠️ Hands-On Lab: Drug–Target Network Analysis & Pathway Enrichment
- Select a disease and identify associated genes and therapeutic targets.
- Retrieve existing drugs and investigate their molecular targets.
- Construct and visualize protein interaction networks using STRING and Cytoscape.
- Identify relevant biological pathways and potential drug repurposing opportunities.
Hands-On Tools: Open Targets | ChEMBL | PubChem | STRING | Cytoscape | Reactome
📅 Day 2 – Biomedical Knowledge Graphs, Graph Embeddings & AI-Based Drug–Disease Prediction
- Introduction to biomedical knowledge graphs and their applications in drug discovery.
- Representation of drugs, genes, diseases, pathways, and adverse effects through graph entities and relationships.
- Biomedical knowledge graph construction and visualization using NetworkX.
- Understanding graph embeddings and relational learning approaches.
- Introduction to TransE, DistMult, and ComplEx graph embedding models.
- Drug–disease link prediction and computational candidate ranking.
- Interpretation of prediction scores and graph-based biological relationships.
- Model evaluation and limitations of computational therapeutic predictions.
🛠️ Hands-On Lab: Biomedical Knowledge Graph Analysis & Link Prediction
- Import a curated drug–gene–disease dataset into Google Colab.
- Construct and visualize a biomedical knowledge graph using Python and NetworkX.
- Explore graph embeddings through a guided TransE experiment.
- Evaluate predicted drug–disease links and compare candidate scores.
Hands-On Tools: Google Colab | Python | Pandas | NetworkX | PyKEEN | Matplotlib
📅 Day 3 – AI-Driven Drug Repurposing, Therapeutic Candidate Prioritization & Virtual Live-Lab Experimentation
- Integration of network pharmacology and biomedical knowledge graph predictions.
- Evidence-weighted scoring and prioritization of potential drug–disease associations.
- Interpretation of therapeutic targets, molecular pathways, and biological plausibility.
- Drug safety signals and adverse drug effect assessment.
- Literature-based validation of potential therapeutic associations using PubMed.
- Evaluation of available clinical evidence using ClinicalTrials.gov.
- Comparison of graph embedding approaches and prediction results.
- Development of research hypotheses and experimental validation strategies.
🛠️ Hands-On Lab 1: Drug Candidate Prioritization & Biomedical Evidence Analysis
- Investigate predicted drug–disease associations and their molecular targets.
- Evaluate supporting therapeutic evidence using Open Targets and ChEMBL.
- Review relevant literature and clinical trial information.
- Prioritize potential candidates based on biological relevance and supporting evidence.
Hands-On Tools: Open Targets | ChEMBL | PubMed | ClinicalTrials.gov
🧪 Hands-On Lab 2: Repurpose Drug Discovery Lab – Virtual Research Project
Participants will conduct a guided drug repurposing experiment using the Repurpose Drug Discovery Lab at live-labs.org.
- Select a drug–disease case study and define research objectives.
- Validate biomedical data and retrieve supporting evidence.
- Explore drug–gene–disease relationships using knowledge graphs.
- Analyze drug–disease predictions and rank potential candidates.
- Generate a research report with supporting evidence and testable hypotheses.
Hands-On Tools: Repurpose Drug Discovery Lab | Biomedical Knowledge Graphs | Link Prediction | Drug Candidate Ranking
Who Should Enrol?
- Pharmacy and Pharmaceutical Sciences Students
- Pharmacology and Drug Discovery Researchers
- Bioinformatics and Computational Biology Researchers
- Biotechnology and Biomedical Science Researchers
- Network Pharmacology and Systems Biology Researchers
- Artificial Intelligence and Machine Learning Researchers
- Medicinal Chemistry and Molecular Medicine Researchers
- Precision Medicine and Translational Research Professionals
- Ph.D. Scholars and Postdoctoral Researchers
- Faculty Members and Academicians
- Pharmaceutical and Biotechnology Industry Professionals
Important Dates
Registration Ends
November 3, 2026
IST
Workshop Dates
November 3, 2026 – November 5, 2026
IST 5:30 PM
Workshop Outcomes
After completing the workshop, participants will be able to:
- Identify disease-associated genes and potential therapeutic targets.
- Retrieve and interpret drug–target interactions and bioactivity information.
- Construct protein interaction networks and perform pathway enrichment analysis.
- Apply network pharmacology methods to investigate drug repurposing opportunities.
- Construct and interpret biomedical knowledge graphs.
- Explore graph embedding models and drug–disease link prediction.
- Evaluate potential therapeutic candidates using prediction scores and biomedical evidence.
- Investigate drug safety information and supporting research literature.
- Perform a guided drug repurposing experiment using the Repurpose Drug Discovery Lab.
- Generate evidence-based research reports and develop testable therapeutic hypotheses.
Fee Structure
Student Fee
₹2999 | $75
Ph.D. Scholar / Researcher Fee
₹3999 | $85
Academician / Faculty Fee
₹4999 | $95
Industry Professional Fee
₹5999 | $115
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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