ArticleQuantitative biology (Beijing, China)2024
DDI-Transform: A neural network for predicting drug-drug interaction events.
Article in Quantitative biology (Beijing, China), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Enabling Drug-Drug Interaction Event Prediction with Multi-view-enhanced Chemical Structural Information.Interdisciplinary sciences, computational life sciences · 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
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- A graph attention-based deep learning network for predicting biotech-small-molecule drug interactions.Bioinformatics advances · 2025Article
- DDI-Transform: A neural network for predicting drug-drug interaction events.Quantitative biology (Beijing, China) · 2024Article
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Authors and funding
2 authors.
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Abstract
Drug-drug interaction (DDI) event prediction is a challenging problem, and accurate prediction of DDI events is critical to patient health and new drug development. Recently, many machine learning-based techniques have been proposed for predicting DDI events. However, most of the existing methods do not effectively integrate the multidimensional features of drugs and provide poor mitigation of noise to get effective feature information. To address these limitations, we propose a DDI-Transform neural network framework for DDI event prediction. In DDI-Transform, we design a drug structure information feature extraction module and a drug bind-protein feature extraction module to obtain multidimensional feature information. A stack of DDI-Transform layers in the DDI-Transform network module are then used for adaptive learning, thus adaptively selecting the effective feature information for prediction. The results show that DDI-Transform can accurately predict DDI events and outperform the state-of-the-art models. Results on different scale datasets confirm the robustness of the method.
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