ArticleHealthcare informatics research2020
Prediction of Drug-Drug Interactions by Using Profile Fingerprint Vectors and Protein Similarities.
Article in Healthcare informatics research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 17 citations in OpenAlex.
- Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature.Journal of Taibah University Medical Sciences · 2023Article
- A Rule-Based Inference Framework to Explore and Explain the Biological Related Mechanisms of Potential Drug-Drug Interactions.Computational and mathematical methods in medicine · 2022Article
- Estimating the Optimal Dexketoprofen Pharmaceutical Formulation with Machine Learning Methods and Statistical Approaches.Healthcare informatics research · 2021Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesDrug-drug interaction (DDI) is a vital problem that threatens people's health. However, the prediction of DDIs through
methodsIn this study, widely known similarity-based computational DDI prediction methods were utilized to discover novel potential DDIs. More specifically, known interactions, drug targets, adverse effects, and protein similarities of drug pairs were used to construct drug fingerprints for the prediction of DDIs.
resultsUsing the drug interaction profile, our approach achieved an area under the curve (AUC) of 0.975 in the prediction of a potential DDI. The drug adverse effect profile and protein profile similarity-based methods resulted in AUC values of 0.685 and 0.895, respectively, in the prediction of DDIs.
conclusionsIn this study, we developed a computational approach to the prediction of potential drug interactions. The performance of the similarity-based computational methods was comparatively evaluated using a comprehensive real-world DDI dataset. The evaluations showed that the drug interaction profile information is a better predictor of DDIs compared to drug adverse effects and protein similarities among DDI pairs.
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