is a Knowledge Graph.
MechRepoNet (Mechanistic Repositioning Network) is a knowledge graph that integrates multiple biomedical data sources to support drug repositioning via mechanistic inference.
biomedical, drug discovery, pharmacology, translational
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| ID | Name | URL | Category | Format | Description |
|---|---|---|---|---|---|
| mechreponet.kg | MechRepoNet Knowledge Graph | publication | ❔ | The MechRepoNet knowledge graph in it... | |
| mechreponet.code | MechRepoNet Code | MechRepoNet | ❔ | Python code for building and analyzin... | |
| ID | Name | URL | Category | Format | Description |
|---|---|---|---|---|---|
| mind.train ⚠ | MIND Training Data | train.txt | GraphProduct | tsv | Training data for the MIND knowledge ... |
| mind.test ⚠ | MIND Test Data | test.txt | GraphProduct | tsv | Test data for the MIND knowledge grap... |
| mind.valid ⚠ | MIND Validation Data | valid.txt | GraphProduct | tsv | Validation data for the MIND knowledg... |
| mind.entities | MIND Entities Dictionary | entities.dict (5.4 MB) | Product | tsv | Dictionary of entities in the MIND kn... |
| mind.relations | MIND Relations Dictionary | relations.dict (1.6 KB) | Product | tsv | Dictionary of relations in the MIND k... |
MechRepoNet (Mechanistic Repositioning Network) is a knowledge graph created for the purpose of drug repositioning via mechanistic inference. It integrates multiple biomedical data sources to provide a comprehensive network of compounds, diseases, genes, pathways, and other biological entities.
MechRepoNet was developed by researchers at the Su Lab to enable mechanism-based drug repositioning. The knowledge graph integrates data from various sources including:
The graph contains relationships between compounds, genes, diseases, pathways, GO terms, and other biological entities, allowing for mechanistic inference for drug repositioning.
The MechRepoNet knowledge graph is fully described in:
Mayers, M., Tu, R. (2022). MechRepoNet, a network-based tool for mechanism-based repositioning of small molecules. Bioinformatics, 38(15), 3746-3748. https://doi.org/10.1093/bioinformatics/btac431
Created: April 12, 2022 | Last modified: July 17, 2025