ArticleJournal of cheminformatics2019
KMR: knowledge-oriented medicine representation learning for drug-drug interaction and similarity computation.
Article in Journal of cheminformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A substructure-aware graph neural network incorporating relation features for drug-drug interaction prediction.Quantitative biology (Beijing, China) · 2024Article
- Analysis and identification of drug similarity through drug side effects and indications data.BMC medical informatics and decision making · 2023Article
- Drug-Drug Interactions Prediction Using Fingerprint Only.Computational and mathematical methods in medicine · 2022Article
- Multitype drug interaction prediction based on the deep fusion of drug features and topological relationships.PloS one · 2022Article
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Authors and funding
7 authors.
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Abstract
Efficient representations of drugs provide important support for healthcare analytics, such as drug-drug interaction (DDI) prediction and drug-drug similarity (DDS) computation. However, incomplete annotated data and drug feature sparseness create substantial barriers for drug representation learning, making it difficult to accurately identify new drug properties prior to public release. To alleviate these deficiencies, we propose KMR, a knowledge-oriented feature-driven method which can learn drug related knowledge with an accurate representation. We conduct series of experiments on real-world medical datasets to demonstrate that KMR is capable of drug representation learning. KMR can support to discover meaningful DDI with an accuracy rate of 92.19%, demonstrating that techniques developed in KMR significantly improve the prediction quality for new drugs not seen at training. Experimental results also indicate that KMR can identify DDS with an accuracy rate of 88.7% by facilitating drug knowledge, outperforming existing state-of-the-art drug similarity measures.
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