ReviewiScience2024
Drug-drug interactions prediction based on deep learning and knowledge graph: A review.
Review in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- Deterministic and stochastic interventions in reducing drug-drug interactions in inappropriate prescribing: A systematic review.PloS one · 2026Pooled it
- Molecular Mechanisms of Acute Drug Toxicity in Polypharmacy: Analgesic-Psychotropic Interactions.International journal of molecular sciences · 2026Review
- Pulmonary Drug Delivery in the Era of Nanomedicine: From Biological Barriers to Artificial Intelligence-Driven Optimization.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Machine learning and deep learning-based drug-drug interactions prediction: a systematic review focused on anticancer drugs.NPJ precision oncology · 2026Article
- Immunological Drug-Drug Interactions in Immune Checkpoint Inhibitor Therapy: Mechanisms, Clinical Evidence, and Artificial Intelligence.Current oncology reports · 2026Review
- A comprehensive review of neural network-based approaches for drug-target interaction prediction.Molecular diversity · 2026Review
- LKE-DTA: predicting drug-target binding affinity with large language model representations and knowledge graph embeddings.Molecular diversity · 2026Article
- NovelHTI: An Interpretable Pathway-Enhanced Framework for De Novo Target Prediction of Medicinal Herbs via Cross-Scale Heterogeneous Information Fusion.Pharmaceuticals (Basel, Switzerland) · 2026Article
- BEACON: predicting side effects and therapeutics outcomes to drugs by Bridging knowlEdge grAph with CONtextual language model.bioRxiv : the preprint server for biology · 2026Article
- Advancing Drug-Drug Interaction Prediction with Biomimetic Improvements: Leveraging the Latest Artificial Intelligence Techniques to Guide Researchers in the Field.Biomimetics (Basel, Switzerland) · 2026Review
- TRACE-DDI: A Hybrid Framework of Transformer-GAT Context Encoder and Pathway-Anchored Knowledge Graphs for DDI Prediction.Computational and structural biotechnology journal · 2026Article
- KG-DFI: A Prediction of Drug-Food Interactions Based on Knowledge Graph Embedding.Computational and structural biotechnology journal · 2026Article
- Structure-Property Associations: Breaking Paradigms for Linking Chemical Structures and Biological Properties in Drug Discovery.ChemMedChem · 2026Article
- Construction and Applications of Knowledge Graphs in Ophthalmology.Ophthalmic research · 2026Review
- Drug-drug interaction identification using large language models.medRxiv : the preprint server for health sciences · 2025Article
- Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations.Pharmaceutics · 2025Review
- SCATrans: semantic cross-attention transformer for drug-drug interaction predication through multimodal biomedical data.BMC bioinformatics · 2025Article
- Drug-drug interaction prediction of traditional Chinese medicine based on graph attention networks.Scientific reports · 2025Article
- Drug repositioning model based on knowledge graph embedding.Scientific reports · 2025Article
- Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions.Pharmaceuticals (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-drug interactions (DDIs) can produce unpredictable pharmacological effects and lead to adverse events that have the potential to cause irreversible damage to the organism. Traditional methods to detect DDIs through biological or pharmacological analysis are time-consuming and expensive, therefore, there is an urgent need to develop computational methods to effectively predict drug-drug interactions. Currently, deep learning and knowledge graph techniques which can effectively extract features of entities have been widely utilized to develop DDI prediction methods. In this research, we aim to systematically review DDI prediction researches applying deep learning and graph knowledge. The available biomedical data and public databases related to drugs are firstly summarized in this review. Then, we discuss the existing drug-drug interactions prediction methods which have utilized deep learning and knowledge graph techniques and group them into three main classes: deep learning-based methods, knowledge graph-based methods, and methods that combine deep learning with knowledge graph. We comprehensively analyze the commonly used drug related data and various DDI prediction methods, and compare these prediction methods on benchmark datasets. Finally, we briefly discuss the challenges related to drug-drug interactions prediction, including asymmetric DDIs prediction and high-order DDI prediction.
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.