ReviewBioImpacts : BI2016
Computerized techniques pave the way for drug-drug interaction prediction and interpretation.
Review in BioImpacts : BI, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Emerging advancements and expanding technological scope of education and practices in pharmacy and pharmaceutical sciences.BioImpacts : BI · 2026Review
- DDI-CYP: Metabolism ensemble models for drug-drug interaction predictions.Drug metabolism and disposition: the biological fate of chemicals · 2025Article
- A Review on New Frontiers in Drug-Drug Interaction Predictions and Safety Evaluations with In Vitro Cellular Models.Pharmaceutics · 2025Review
- A Molecular Fragment Representation Learning Framework for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning.BMC bioinformatics · 2025Article
- Article
- Drug-drug interactions prediction based on deep learning and knowledge graph: A review.iScience · 2024Review
- MSEDDI: Multi-Scale Embedding for Predicting Drug-Drug Interaction Events.International journal of molecular sciences · 2023Article
- Drug-Drug Interactions in People Living With HIV at Risk of Hepatic and Renal Impairment: Current Status and Future Perspectives.Journal of clinical pharmacology · 2022Review
- Transformative dynamism in pharmaceutical and biomedical research: Complexity of integration of innovative R & D hubs.BioImpacts : BI · 2021Review
- A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning.Frontiers in pharmacology · 2021Review
- Prediction of potential drug interactions between repurposed COVID-19 and antitubercular drugs: an integrational approach of drug information software and computational techniques data.Therapeutic advances in drug safety · 2021Article
- Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis.BioImpacts : BI · 2020Article
- Bispecific therapeutic aptamers for targeted therapy of cancer: a review on cellular perspective.Journal of molecular medicine (Berlin, Germany) · 2018Review
- An attention-based effective neural model for drug-drug interactions extraction.BMC bioinformatics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionHealth care industry also patients penalized by medical errors that are inevitable but highly preventable. Vast majority of medical errors are related to adverse drug reactions, while drug-drug interactions (DDIs) are the main cause of adverse drug reactions (ADRs). DDIs and ADRs have mainly been reported by haphazard case studies. Experimental in vivo and in vitro researches also reveals DDI pairs. Laboratory and experimental researches are valuable but also expensive and in some cases researchers may suffer from limitations.
methodsIn the current investigation, the latest published works were studied to analyze the trend and pattern of the DDI modelling and the impacts of machine learning methods. Applications of computerized techniques were also investigated for the prediction and interpretation of DDIs.
resultsComputerized data-mining in pharmaceutical sciences and related databases provide new key transformative paradigms that can revolutionize the treatment of diseases and hence medical care. Given that various aspects of drug discovery and pharmacotherapy are closely related to the clinical and molecular/biological information, the scientifically sound databases (e.g., DDIs, ADRs) can be of importance for the success of pharmacotherapy modalities.
conclusionA better understanding of DDIs not only provides a robust means for designing more effective medicines but also grantees patient safety.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.