That's a great summary of the potential of network pharmacology in drug repurposing, and your current work sounds highly relevant and impactful. Since you're actively involved in repurposing FDA-approved drugs using network-based approaches, here’s a list of commonly used tools, databases, and pipelines that researchers in this area rely on for effective results:

🔧 Tools & Platforms

Cytoscape

For visualizing and analyzing complex biological interaction networks.

Plugin support: ClueGO, CyTargetLinker, stringApp.

Gephi

Another network visualization tool, often used for large-scale networks.

NetworkAnalyst

A web-based tool for comprehensive network-based visual analytics and meta-analysis.

GSEA (Gene Set Enrichment Analysis)

Useful for identifying biological pathways enriched in your dataset.

R packages (e.g., igraph, BioNet, tidygraph)

For advanced custom network modeling and statistical analysis.

🧬 Databases for Drug–Gene–Disease Information

DrugBank

Detailed information on drugs, targets, interactions, mechanisms of action.

PharmGKB

Pharmacogenomics knowledge, gene–drug–disease relationships.

DisGeNET

Comprehensive gene–disease associations, helpful for identifying disease modules.

STRING

Protein–protein interaction data, critical for constructing functional networks.

CTD (Comparative Toxicogenomics Database)

Chemical–gene–disease relationships; great for environmental or toxicity aspects.

BindingDB

Affinity data of drug-target interactions for modeling pharmacodynamics.

LINCS (Library of Integrated Network-based Cellular Signatures)

Perturbational data (gene expression profiles) from small molecule treatments.

Open Targets Platform

Integrates genomics and drug-target data for target identification.

🧪 Pipelines & Frameworks

RepurposeDB (via CANDO)

Integrates cheminformatics and systems-based approaches for repurposing.

CANDO (Computational Analysis of Novel Drug Opportunities)

Predicts multi-target behavior of drugs across diseases.

DrugRepV2

Integrates various omics data and ML for drug repurposing predictions.

DeepPurpose (Python Library)

Deep learning toolkit for drug–target interaction prediction.

L1000FWD / Connectivity Map (CMap)

To identify drugs that reverse disease-associated gene expression signatures.

🧠 Tips from Ongoing Research Practices

Multi-Omics Integration: Combining transcriptomic, proteomic, and metabolomic data improves confidence in target predictions.

Use of ML/AI Models: Graph neural networks (e.g., GraphSAGE, GAT) are now increasingly used to predict drug-disease links.

Cross-validation with clinical data: Use real-world evidence (e.g., from ClinicalTrials.gov) to support predicted repositioning candidates.

If you're open to collaboration or data sharing, posting in forums like BioStars, ResearchGate, or joining Slack groups like Network Biology Community or Systems Pharmacology can help connect with peers working on similar problems.

Komalkumar Chandan

Hi !

I truely feel network pharmacology, AI and machine learning is the future of advance drug deliveries. In this aspect, Network-based drug repurposing leverages computational tools and databases to systematically explore drug–target–disease interactions, enabling the identification of novel therapeutic indications.There are various bank and effective resources like -DrugBank, ChEMBL, and STITCH for drug–protein interactions, while DisGeNET, OMIM, and CTD provide disease–gene associations. If someone is working on network construction and analysis they can typically perform analysis using tools like Cytoscape (with plugins such as ClueGO, CyTargetLinker), Gephi, and NetworkAnalyst, while pathway enrichment is supported by KEGG, Reactome, and DAVID. Integrated pipelines like LINCS L1000, Open Targets Platform, and Connectivity Map (CMap) facilitate transcriptomic correlation and perturbation-based matching. For machine learning-based prioritization, frameworks such as DeepPurpose, RepurposeDB, and Drug Repurposing Hub are valuable. These tools together allow for high-confidence prediction of drug repurposing candidates, especially in polygenic and multifactorial diseases.

Small learning of different tools as per your needs and field can surely add quality to your results by definitely saving lots of time and money!

I would be happy to collaborate with you in any of your ongoing projects (Research,review,books)- Network Pharmacology in Accelerating Drug Repurposing for Complex Diseases.