AIMC Topic: Drug Interactions

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MEN: leveraging explainable multimodal encoding network for precision prediction of CYP450 inhibitors.

Scientific reports Jul 1, 2025
Drug-drug interactions (DDIs) present serious risks in clinical settings, especially for patients who are prescribed multiple medications. A major factor contributing to these interactions is the inhibition of cytochrome P450 (CYP450) enzymes, which ...
Humans Machine Learning Drug Interactions Cytochrome P-450 Enzyme System Cytochrome P-450 Enzyme Inhibitors
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SCATrans: semantic cross-attention transformer for drug-drug interaction predication through multimodal biomedical data.

BMC bioinformatics Jun 10, 2025
Predicting potential drug-drug interactions (DDIs) from biomedical data plays a critical role in drug therapy, drug development, drug regulation, and public health. However, it remains challenging due to the large number of possible drug combinations...
Humans Algorithms Deep Learning Computational Biology Drug Interactions Semantics
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HLN-DDI: hierarchical molecular representation learning with co-attention mechanism for drug-drug interaction prediction.

BMC bioinformatics Jun 4, 2025
BACKGROUND: Accurate identification of drug-drug interactions (DDIs) is critical in pharmacology, as DDIs can either enhance therapeutic efficacy or trigger adverse reactions when multiple medications are administered concurrently. Traditional method...
Humans Machine Learning Algorithms Computational Biology Drug Interactions
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Drug-drug interaction prediction of traditional Chinese medicine based on graph attention networks.

Scientific reports May 28, 2025
Predicting drug-drug interactions (DDI) is crucial for preventing adverse reactions in patients and plays a vital role in drug design and development. However, traditional Chinese medicine (TCM) formulations, typically composed of multiple herbal ing...
Humans Neural Networks, Computer Deep Learning Medicine, Chinese Traditional Drugs, Chinese Herbal Drug Interactions
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Taco-DDI: accurate prediction of drug-drug interaction events using graph transformer-based architecture and dynamic co-attention matrices.

Neural networks : the official journal of the International Neural Network Society May 20, 2025
Drug-drug interactions (DDIs) are critical in pharmaceutical research, as adverse interactions can pose significant risks for patient treatment plans. Accurate prediction of DDI events risk levels can provide valuable guidance for designing safer and...
Humans Neural Networks, Computer Machine Learning Algorithms Drug Interactions
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TransformDDI: The Transformer-Based Joint Multi-Task Model for End-to-End Drug-Drug Interaction Extraction.

IEEE journal of biomedical and health informatics Apr 4, 2025
Drug-Drug Interactions (DDI) identification is a part of the drug safety process, that focuses at avoiding potential adverse drug effects that can lead to patient health risks. With the exponential growth in published literature, it becomes increasin...
Humans Machine Learning Algorithms Natural Language Processing Drug Interactions Drug-Related Side Effects and Adverse Reactions Data Mining
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Drug-drug interaction prediction based on graph contrastive learning and dual-view fusion.

Computational biology and chemistry Mar 21, 2025
Drug-drug interaction (DDI) is important in drug research and are one of the major causes of morbidity and mortality. The deep learning methods can automatically extract drug features from molecular graphs or drug-related networks, which improves the...
Humans Deep Learning Drug Interactions
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A novel deep sequential learning architecture for drug drug interaction prediction using DDINet.

Scientific reports Mar 18, 2025
Drug drug Interactions (DDI) present considerable challenges in healthcare, often resulting in adverse effects or decreased therapeutic efficacy. This article proposes a novel deep sequential learning architecture called DDINet to predict and classif...
Humans Algorithms Deep Learning Drug Interactions
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An Optimised Mobilenet V2 Attention Parallel Network for Predicting Drug-Drug Interactions Through Combining Local and Global Features.

Biopharmaceutics & drug disposition Mar 12, 2025
Drug-drug interactions (DDIs) are an important concern in the clinical practice and drug development process as these may lead to serious adverse effects on patient safety. Thorough DDI prediction is important for effective medication management and ...
Humans Neural Networks, Computer Drug Interactions
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MOLGAECL: Molecular Graph Contrastive Learning via Graph Auto-Encoder Pretraining and Fine-Tuning Based on Drug-Drug Interaction Prediction.

Journal of chemical information and modeling Mar 11, 2025
Drug-drug interactions influence drug efficacy and patient prognosis, providing substantial research value. Some existing methods struggle with the challenges posed by sparse networks or lack the capability to integrate data from multiple sources. In...
Humans Machine Learning Drug Interactions
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