ReviewBriefings in bioinformatics2023
Drug-drug interaction prediction: databases, web servers and computational models.
Review in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks.Frontiers in pharmacology · 2026Pooled it
- DDI-HierPred: An Artificial Intelligence-Based Hierarchical PK/PD Platform for Drug-Drug Interaction Prediction.Pharmaceutics · 2026Article
- Graph-Based Machine Learning for Predicting Drug-Drug Interactions: A Systematic Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Machine learning and deep learning-based drug-drug interactions prediction: a systematic review focused on anticancer drugs.NPJ precision oncology · 2026Article
- FSSM-DDI: Fusion State Space Model for predicting drug-drug interaction using social-media and drug descriptions.BMC biology · 2026Article
- Beneficial Herb-Drug Interaction of Shenqi Oral Liquid on Atorvastatin: A Pharmacokinetic and Pharmacodynamic Investigation in Rats with Hyperlipidemia.Chinese journal of integrative medicine · 2026Article
- On the existence conditions of interaction indices in response surface models.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- A comprehensive review of cluster methods for drug-drug interaction network.Quantitative biology (Beijing, China) · 2026Review
- MFCN-DDI: Capsule network based on multimodal feature for multitype drug-drug interaction prediction.Quantitative biology (Beijing, China) · 2026Article
- BCheM: a framework integrating molecular attributes and behavioral representations for precise prediction of drug resistance across multiple disease contexts.Frontiers in bioinformatics · 2026Article
- Research Progress on the Biological Activities and Clinical Applications of Pseudoprotodioscin.Current issues in molecular biology · 2025Review
- PBIP: a deep learning framework for predicting phage-bacterium interactions at the strain level.Briefings in bioinformatics · 2025Article
- PBIP: a deep learning framework for predicting phage-bacterium interactions at the strain level.Briefings in bioinformatics · 2025Article
- Rapid discovery of drug-introduced multiple organ dysfunctionActa pharmaceutica Sinica. B · 2025Article
- InterPAD is a database of drug-drug interaction between phytochemicals and anticancer drugs.Scientific reports · 2025Article
- Integrative transcriptome-based drug repurposing in tuberculosis.bioRxiv : the preprint server for biology · 2025Article
- In Vitro Evaluation of Drug-Drug Interaction Between Gliclazide and Antacids at the Absorption Level.Pharmaceuticals (Basel, Switzerland) · 2025Article
- ADCNet: a unified framework for predicting the activity of antibody-drug conjugates.Briefings in bioinformatics · 2025Article
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 at 2 institutions in 1 country.
Funding
Abstract
In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.
Indexed as
Identifiers
What OpenQuestion holds
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.