ArticleScientific reports2022
Prediction of drug-drug interaction events using graph neural networks based feature extraction.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 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
- Beyond Structural Representations: A Semantic-Relational Graph Neural Network for Polypharmacy Side Effect Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations.npj drug discovery · 2026Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions.Pharmaceuticals (Basel, Switzerland) · 2025Review
- A comprehensive review of deep learning-based approaches for drug-drug interaction prediction.Briefings in functional genomics · 2025Review
- Multimodal graph neural networks in healthcare: a review of fusion strategies across biomedical domains.Frontiers in artificial intelligence · 2025Review
- Advancing drug-drug interactions research: integrating AI-powered prediction, vulnerable populations, and regulatory insights.Frontiers in pharmacology · 2025Review
- PSO-FeatureFusion: a general framework for fusing heterogeneous features via particle swarm optimization.Bioinformatics advances · 2025Article
- Predicting the toxic side effects of drug interactions using chemical structures and protein sequences.Scientific reports · 2024Article
- Deep learning for drug-drug interaction prediction: A comprehensive review.Quantitative biology (Beijing, China) · 2024Review
- Learning self-supervised molecular representations for drug-drug interaction prediction.BMC bioinformatics · 2024Article
- A Novel Deep Learning Model for Drug-drug Interactions.Current computer-aided drug design · 2024Article
- Article
- Bridging the Worlds of Pharmacometrics and Machine Learning.Clinical pharmacokinetics · 2023Review
- DPSP: a multimodal deep learning framework for polypharmacy side effects prediction.Bioinformatics advances · 2023Article
- Graph attention network predicts drug-gene associations of matrix metalloproteinases 9-based host modulation in periodontitis.Journal of Indian Society of PeriodontologyArticle
Corrections and comments
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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
The prevalence of multi_drug therapies has been increasing in recent years, particularly among the elderly who are suffering from several diseases. However, unexpected Drug_Drug interaction (DDI) can cause adverse reactions or critical toxicity, which puts patients in danger. As the need for multi_drug treatment increases, it's becoming increasingly necessary to discover DDIs. Nevertheless, DDIs detection in an extensive number of drug pairs, both in-vitro and in-vivo, is costly and laborious. Therefore, DDI identification is one of the most concerns in drug-related researches. In this paper, we propose GNN-DDI, a deep learning-based method for predicting DDI-associated events in two stages. In the first stage, we collect the drugs information from different sources and then integrate them through the formation of an attributed heterogeneous network and generate a drug embedding vector based on different drug interaction types and drug attributes. In the second stage, we aggregate the representation vectors then predictions of the DDIs and their events are performed through a deep multi-model framework. Various evaluation results show that the proposed method can outperform state-of-the methods in the prediction of drug-drug interaction-associated events. The experimental results indicate that producing the drug's representations based on different drug interaction types and attributes is efficient and effective and can better show the intrinsic characteristics of a drug.
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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.