ArticleBMC medical informatics and decision making2023
Analysis and identification of drug similarity through drug side effects and indications data.
Article in BMC medical informatics and decision making, 2023. 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.
- Human Dignity and Artificial Intelligence in Healthcare: A Basis for a Catholic Ethics on AI.Journal of religion and health · 2026Article
- From class effects to specificity FAERS evidence and network mapping of adverse events in NSCLC targeted therapy.International journal of surgery (London, England) · 2026Article
- Multi-DDA: drug-disease association prediction using a hybrid graph convolutional network with multi-modal drug representations.Bioinformatics advances · 2026Article
- Higher Selective Targeting of Telomeric Multimeric G-quadruplex by Natural Product Berberine.Current medicinal chemistry · 2026Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundThe measurement of drug similarity has many potential applications for assessing drug therapy similarity, patient similarity, and the success of treatment modalities. To date, a family of computational methods has been employed to predict drug-drug similarity. Here, we announce a computational method for measuring drug-drug similarity based on drug indications and side effects.
methodsThe model was applied for 2997 drugs in the side effects category and 1437 drugs in the indications category. The corresponding binary vectors were built to determine the Drug-drug similarity for each drug. Various similarity measures were conducted to discover drug-drug similarity.
resultsAmong the examined similarity methods, the Jaccard similarity measure was the best in overall performance results. In total, 5,521,272 potential drug pair's similarities were studied in this research. The offered model was able to predict 3,948,378 potential similarities.
conclusionBased on these results, we propose the current method as a robust, simple, and quick approach to identifying drug similarity.
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