August 12, 2025Computers in Biology and MedicineOpen Access

The use of knowledge graphs for drug repurposing: From classical machine learning algorithms to graph neural networks

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Authors

SWShuai WeiZhengzhou University CSChristo SasiRadboud University Nijmegen JPJelle PiepenbrockRadboud University Nijmegen

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Implication

This review highlights drug repurposing potentials in existing treatments, suggesting knowledge graphs may enhance predictions using machine and deep learning.

Key Points

Knowledge graphs significantly improve drug repurposing by efficiently modeling drug-disease relationships, enabling better predictions.
Machine learning techniques combined with graph neural networks enhance the identification of new therapeutic indications for existing drugs.
The review analyzes various computational methods for drug repurposing, detailing their advantages and limitations in utilizing knowledge graphs.
Adopting knowledge graphs in drug development may lead to innovative treatment options and streamline the research process.

Cite This Study

Wei et al. (2025) studied this question.

synapsesocial.com/papers/68a360f20a429f7973329bcd https://doi.org/10.1016/j.compbiomed.2025.110873

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Implication

This review highlights drug repurposing potentials in existing treatments, suggesting knowledge graphs may enhance predictions using machine and deep learning.

The use of knowledge graphs for drug repurposing: From classical machine learning algorithms to graph neural networks

Discussion

Member takes

Implication

Key Points

Cite This Study

The use of knowledge graphs for drug repurposing: From classical machine learning algorithms to graph neural networks

Discussion

Member takes

Implication

Key Points

Cite This Study

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