KG-Predict
KG-Predict is a knowledge graph computational framework for drug repurposing that integrates multiple types of genotypic and phenotypic data. The framework constructs GP-KG (Genotype-Phenotype Knowledge Graph), containing 1,246,726 associations between 61,146 biomedical entities from various databases. KG-Predict uses graph embedding methods to learn low-dimensional representations of entities and relations, enabling inference of new drug-disease interactions. The system has been validated for identifying repositioned candidate drugs, particularly for Alzheimer's disease, achieving high performance metrics (AUROC = 0.981, AUPR = 0.409) and successfully prioritizing FDA-approved and clinical trial anti-AD drugs.
[]
[{"category": "GraphProduct", "description": "GP-KG knowledge graph containing 1,246,726 associations between 61,146 entities from multiple genotypic and phenotypic databases", "format": "csv", "id": "kg-predict.gpkg", "name": "GP-KG Knowledge Graph Data", "original_source": ["kg-predict"], "product_url": "http://nlp.case.edu/public/data/GPKG-Predict/data/"}, {"category": "ProcessProduct", "description": "Python implementation of KG-Predict framework for graph embedding and drug repurposing prediction", "id": "kg-predict.code", "name": "KG-Predict Code", "original_source": ["kg-predict"], "product_url": "http://nlp.case.edu/public/data/GPKG-Predict/code/"}, {"category": "Product", "description": "Alzheimer's disease case study predictions and clinical trial evidence", "format": "csv", "id": "kg-predict.ad_predictions", "name": "AD Drug Predictions", "original_source": ["kg-predict"], "product_url": "http://nlp.case.edu/public/data/GPKG-Predict/"}]
{"activity_status": "active", "category": "KnowledgeGraph", "creation_date": "2025-11-22T00:00:00Z", "description": "KG-Predict is a knowledge graph computational framework for drug repurposing that integrates multiple types of genotypic and phenotypic data. The framework constructs GP-KG (Genotype-Phenotype Knowledge Graph), containing 1,246,726 associations between 61,146 biomedical entities from various databases. KG-Predict uses graph embedding methods to learn low-dimensional representations of entities and relations, enabling inference of new drug-disease interactions. The system has been validated for identifying repositioned candidate drugs, particularly for Alzheimer's disease, achieving high performance metrics (AUROC = 0.981, AUPR = 0.409) and successfully prioritizing FDA-approved and clinical trial anti-AD drugs.", "domains": ["drug discovery", "systems biology", "biomedical", "pharmacology", "translational", "genomics", "phenotype"], "evaluation_page": "resource/kg-predict/kg-predict_eval_automated.html", "homepage_url": "http://nlp.case.edu/public/data/GPKG-Predict/", "id": "kg-predict", "last_modified_date": "2025-11-22T00:00:00Z", "layout": "resource_detail", "license": {"id": "https://www.elsevier.com/open-access/userlicense/1.0/", "label": "Elsevier User License"}, "name": "KG-Predict", "products": [{"category": "GraphProduct", "description": "GP-KG knowledge graph containing 1,246,726 associations between 61,146 entities from multiple genotypic and phenotypic databases", "format": "csv", "id": "kg-predict.gpkg", "name": "GP-KG Knowledge Graph Data", "original_source": ["kg-predict"], "product_url": "http://nlp.case.edu/public/data/GPKG-Predict/data/"}, {"category": "ProcessProduct", "description": "Python implementation of KG-Predict framework for graph embedding and drug repurposing prediction", "id": "kg-predict.code", "name": "KG-Predict Code", "original_source": ["kg-predict"], "product_url": "http://nlp.case.edu/public/data/GPKG-Predict/code/"}, {"category": "Product", "description": "Alzheimer's disease case study predictions and clinical trial evidence", "format": "csv", "id": "kg-predict.ad_predictions", "name": "AD Drug Predictions", "original_source": ["kg-predict"], "product_url": "http://nlp.case.edu/public/data/GPKG-Predict/"}], "publications": [{"authors": ["Gao Z", "Ding P", "Xu R"], "doi": "10.1016/j.jbi.2022.104133", "id": "PMID:35690340", "journal": "Journal of Biomedical Informatics", "title": "KG-Predict: A knowledge graph computational framework for drug repurposing", "year": "2022"}], "synonyms": ["GPKG-Predict", "GP-KG"], "taxon": ["NCBITaxon:9606"]}