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A comprehensive large-scale biomedical knowledge graph for AI-powered data-driven biomedical research

Nature Machine Intelligence volume 7, pages 602–614 (2025)Cite this article

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Abstract

To address the rapid growth of scientific publications and data in biomedical research, knowledge graphs (KGs) have become a critical tool for integrating large volumes of heterogeneous data to enable efficient information retrieval and automated knowledge discovery. However, transforming unstructured scientific literature into KGs remains a significant challenge, with previous methods unable to achieve human-level accuracy. Here we used an information extraction pipeline that won first place in the LitCoin Natural Language Processing Challenge (2022) to construct a large-scale KG named iKraph using all PubMed abstracts. The extracted information matches human expert annotations and significantly exceeds the content of manually curated public databases. To enhance the KG’s comprehensiveness, we integrated relation data from 40 public databases and relation information inferred from high-throughput genomics data. This KG facilitates rigorous performance evaluation of automated knowledge discovery, which was infeasible in previous studies. We designed an interpretable, probabilistic-based inference method to identify indirect causal relations and applied it to real-time COVID-19 drug repurposing from March 2020 to May 2023. Our method identified around 1,200 candidate drugs in the first 4 months, with one-third of those discovered in the first 2 months later supported by clinical trials or PubMed publications. These outcomes are very challenging to attain through alternative approaches that lack a thorough understanding of the existing literature. A cloud-based platform (https://biokde.insilicom.com) was developed for academic users to access this rich structured data and associated tools.

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Fig. 1: The coverage of iKraph and some basic properties.
Fig. 2: Drug repurposing for COVID-19.
Fig. 3: Drug repurposing for cystic fibrosis.
Fig. 4: F1 scores for drug repurposing prediction for ten diseases and ten common drugs.
Fig. 5: The overview of our drug repurposing strategy and validation approach.

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Data availability

The datasets used in this study are available on the GitHub repository at https://github.com/myinsilicom/iKraph (ref. 97). Due to size limitations, additional large datasets can be accessed via Zenodo at https://doi.org/10.5281/ZENODO.14846820 (ref. 98). We used the BioRED dataset to train our NER and relation extraction models, and the BioRED dataset can be accessed through https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/. The complete KG is hosted on the cloud-based platform: https://www.biokde.com. The downloadable version of the complete iKraph can be accessed via Zenodo at https://doi.org/10.5281/ZENODO.14846820 (ref. 98). Source data are provided with this paper.

Code availability

The code and datasets generated during this study can be found via the GitHub repository at https://github.com/myinsilicom/iKraph (ref. 97).

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Acknowledgements

We thank the LitCoin NLP Challenge and BioCreative Challenge VIII BioRED track organizers for generating the valuable challenge dataset, which made the work possible. This research was partially supported by the NIH under grant no. R21LM014277 (J. Zhang), contract 75N91024C00007 (J. Zhang) and contract 75N93024C00034 (J. Zhang); by the National Science Foundation under grant nos. 2335357 (J. Zhang) and 2403911 (J. Zhang) and by the National Cancer Institute, NIH, under Prime Contract No. 75N91019D00024, Task Order No. 75N91024F00030 (J. Zhang). The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government. The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the paper.

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  1. These authors contributed equally: Yuan Zhang, Xin Sui, Feng Pan, Kaixian Yu.

Authors and Affiliations

  1. Department of Statistics, Florida State University, Tallahassee, FL, USA

    Yuan Zhang, Xin Sui, Keqiao Li, Shubo Tian, Arslan Erdengasileng, Qing Han, Wanjing Wang & Jinfeng Zhang

  2. Insilicom LLC, Tallahassee, FL, USA

    Yuan Zhang, Feng Pan, Kaixian Yu, Jianan Wang, Donghu Sun, Henry Chung, Jun Zhou, Eric Zhou, Ben Lee, Tingting Zhao & Jinfeng Zhang

  3. Sunnyvale, CA, USA

    Jian Wang

  4. Forward Informatics, Winchester, MA, USA

    Peili Zhang

  5. Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA

    Xing Qiu

  6. Department of Geography, Florida State University, Tallahassee, FL, USA

    Tingting Zhao

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  1. Yuan Zhang
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Y.Z., X.S., F.P., K.L., S.T., A.E., Q.H., W.W., Jianan Wang and Jian Wang collected data and developed models and pipelines. Y.Z., F.P. and J. Zhang analysed the data and developed methods. D.S., H.C., J. Zhou, E.Z., B.L., T.Z. and J. Zhang. developed the iExplore platform interface. K.Y. and J. Zhang conceptualized and designed the study. Y.Z., F.P., K.Y. and J. Zhang wrote the paper. X.Q., T.Z. and P.Z. provided consultation and paper revision. J. Zhang supervised the study and is the corresponding author.

Corresponding author

Correspondence to Jinfeng Zhang.

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Competing interests

J. Zhang and T.Z. are owners of Insilicom LLC. The other authors declare no competing interests.

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Nature Machine Intelligence thanks James Evans and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Methods sections 1–5, Discussion section 6, Figs. 1–3, Tables 1–4 and Box 1.

Reporting Summary

Source data

Source Data Fig. 1

Data used to generate the Venn plot, pie plot and line plot.

Source Data Fig. 2

Data used to generate the plot on repurposed drugs for COVID-19.

Source Data Fig. 3

Data used to generate the plot on repurposed drugs for cystic fibrosis.

Source Data Fig. 4

Data used to generate the plot on drug repurposing study on top ten common drugs and ten diseases.

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Zhang, Y., Sui, X., Pan, F. et al. A comprehensive large-scale biomedical knowledge graph for AI-powered data-driven biomedical research. Nat Mach Intell 7, 602–614 (2025). https://doi.org/10.1038/s42256-025-01014-w

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