Every Cure Knowledge Graph: A Unified Biomedical Knowledge Graph for Drug Repurposing

Abstract

Identifying causal connections between existing drugs and mechanistic profiles of diseases is a foundational step for effective drug repurposing. Although knowledge graphs (KGs) are highly suited for consolidating biomedical databases and tracking these connections, a single biomedical KG is constrained by its ingestion pipeline and knowledge sources. While different biomedical KGs could be complementary if combined, efforts to combine them into a unified and more comprehensive KG are hindered by lack of interoperability and poor provenance. To address those issues, we present EC-KG, a Biolink Model-compatible KG for computational drug repurposing. EC-KG is an interoperable, provenance-first KG which integrates RTX-KG2, ROBOKOP, and PrimeKG at the network-level, encapsulating over 7 million nodes and 81 million edges from 95 primary data sources. EC-KG has improved coverage of core biomedical entities such as drugs, targets, and diseases relevant to drug repurposing vs source graphs, and captures complex biomedical mechanisms within its topology. We demonstrate that the network unification in EC-KG leads to emergence of novel, mechanistically relevant pathways which are disconnected in the underlying constituent networks and show its applications in method development, benchmarking and predictive drug repurposing applications. EC-KG has already been successfully used in drug repurposing research to surface Botulinum Toxin A as a candidate to treat Major Depressive Disorder, as well as to validate repurposing of Lenalidomide and Dexamethasone for a subgroup of patients with Rosai-Dorfman Disease.

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BibTeX RIS

BibTeX

@misc{kaniewski2026every,
  title = {{Every Cure Knowledge Graph: A Unified Biomedical Knowledge Graph for Drug Repurposing}},
  author = {P. Kaniewski and E. K. Carter and D. Rhodes and E. M. Lim and J. Li and J. Vergine and N. Matentzoglu and K. Schaper and J. Reilly and S. Sundar and L. Vijnck and E. Sharp and N. Alfonso and A. Ford and A. Stepanenko and C. Hempstead and P. Brokmeier and C. Bizon and A. Tropsha and M. A. Haendel and D. C. Fajgenbaum and L. Lancashire},
  howpublished = {bioRxiv},
  year = {2026},
  month = aug,
  doi = {10.64898/2026.08.26.747253},
  url = {https://www.biorxiv.org/content/10.64898/2026.08.26.747253v1},
  note = {CC BY-ND},
}

RIS

TY  - UNPB
TI  - Every Cure Knowledge Graph: A Unified Biomedical Knowledge Graph for Drug Repurposing
AU  - P. Kaniewski
AU  - E. K. Carter
AU  - D. Rhodes
AU  - E. M. Lim
AU  - J. Li
AU  - J. Vergine
AU  - N. Matentzoglu
AU  - K. Schaper
AU  - J. Reilly
AU  - S. Sundar
AU  - L. Vijnck
AU  - E. Sharp
AU  - N. Alfonso
AU  - A. Ford
AU  - A. Stepanenko
AU  - C. Hempstead
AU  - P. Brokmeier
AU  - C. Bizon
AU  - A. Tropsha
AU  - M. A. Haendel
AU  - D. C. Fajgenbaum
AU  - L. Lancashire
PY  - 2026
DA  - 2026/08/26
PB  - bioRxiv
DO  - 10.64898/2026.08.26.747253
UR  - https://www.biorxiv.org/content/10.64898/2026.08.26.747253v1
AB  - Identifying causal connections between existing drugs and mechanistic profiles of diseases is a foundational step for effective drug repurposing. Although knowledge graphs (KGs) are highly suited for consolidating biomedical databases and tracking these connections, a single biomedical KG is constrained by its ingestion pipeline and knowledge sources. While different biomedical KGs could be complementary if combined, efforts to combine them into a unified and more comprehensive KG are hindered by lack of interoperability and poor provenance. To address those issues, we present EC-KG, a Biolink Model-compatible KG for computational drug repurposing. EC-KG is an interoperable, provenance-first KG which integrates RTX-KG2, ROBOKOP, and PrimeKG at the network-level, encapsulating over 7 million nodes and 81 million edges from 95 primary data sources. EC-KG has improved coverage of core biomedical entities such as drugs, targets, and diseases relevant to drug repurposing vs source graphs, and captures complex biomedical mechanisms within its topology. We demonstrate that the network unification in EC-KG leads to emergence of novel, mechanistically relevant pathways which are disconnected in the underlying constituent networks and show its applications in method development, benchmarking and predictive drug repurposing applications. EC-KG has already been successfully used in drug repurposing research to surface Botulinum Toxin A as a candidate to treat Major Depressive Disorder, as well as to validate repurposing of Lenalidomide and Dexamethasone for a subgroup of patients with Rosai-Dorfman Disease.
ER  -

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