AIMC Topic: Drug Interactions

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Optimized Drug-Drug Interaction Extraction With BioGPT and Focal Loss-Based Attention.

IEEE journal of biomedical and health informatics Jun 1, 2025
Drug-drug interactions (DDIs) are a significant focus in biomedical research and clinical practice due to their potential to compromise treatment outcomes or cause adverse effects. While deep learning approaches have advanced DDI extraction, challeng...
Databases, Factual Deep Learning Data Mining Drug Interactions Humans Algorithms
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TriCvT-DTI: Predicting Drug-Target Interactions Using Trimodal Representations and Convolutional Vision Transformers.

IEEE journal of biomedical and health informatics Jun 1, 2025
Predicting interactions between drugs and their targets is vital for drug discovery and repositioning. Conventional techniques are slow and labor-intensive, while deep learning algorithms offer efficient solutions. However, deep learning often focus ...
Animals Drug Discovery Neural Networks, Computer Algorithms Pharmaceutical Preparations Drug Interactions Humans Caenorhabditis elegans Deep Learning Image Processing, Computer-Assisted
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SF-Rx: A Multioutput Deep Neural Network-Based Framework Predicting Drug-Drug Interaction under Realistic Conditions for Safe Prescription.

Journal of chemical information and modeling May 12, 2025
Drug-drug interaction (DDI) can compromise therapeutic efficacy and cause detrimental effects in polypharmacy. Computational prediction of DDI has emerged as an alternative approach to time-consuming clinical experiments for investigating potential d...
Humans Neural Networks, Computer Drug Interactions
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[The development of model of prognostication and minimization of risk of by-effects under combined application of agents for treatment of chronic cardiac deficiency using AI].

Problemy sotsial'noi gigieny, zdravookhraneniia i istorii meditsiny May 10, 2025
The chronic cardiac deficiency continues to be one of the leading health care problems requiring innovative solutions. The article presents mathematical algorithm to evaluate drug interactions and targeted to minimize side effects and to optimize chr...
Artificial Intelligence Drug Therapy, Combination Algorithms Models, Theoretical Drug-Related Side Effects and Adverse Reactions Prognosis Chronic Disease Heart Diseases Humans Risk Assessment Drug Interactions
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Knowledge-aware contrastive heterogeneous molecular graph learning.

PLoS computational biology May 1, 2025
Molecular representation learning is pivotal in predicting molecular properties and advancing drug design. Traditional methodologies, which predominantly rely on homogeneous graph encoding, are limited by their inability to integrate external knowled...
Humans Machine Learning Computational Biology Drug Interactions Algorithms Drug Design
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Artificial intelligence to predict inhibitors of drug-metabolizing enzymes and transporters for safer drug design.

Expert opinion on drug discovery May 1, 2025
INTRODUCTION: Drug-metabolizing enzymes (DMEs) and transporters (DTs) play integral roles in drug metabolism and drug-drug interactions (DDIs) which directly impact drug efficacy and safety. It is well-established that inhibition of DMEs and DTs ofte...
Membrane Transport Proteins Drug Development Pharmaceutical Preparations Drug-Related Side Effects and Adverse Reactions Quantitative Structure-Activity Relationship Drug Interactions Animals Humans Drug Design Artificial Intelligence
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Development and Content Analysis Protocol for Evaluating Artificial Intelligence in Drug-Related Information.

Journal of evaluation in clinical practice Feb 1, 2025
INTRODUCTION: Artificial intelligence (AI) has significant transformative potential across various sectors, particularly in health care. This study aims to develop a protocol for the content analysis of a method designed to assess AI applications in ...
Drug Interactions Drug-Related Side Effects and Adverse Reactions Artificial Intelligence Databases, Factual Humans
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A comprehensive review of deep learning-based approaches for drug-drug interaction prediction.

Briefings in functional genomics Jan 15, 2025
Deep learning models have made significant progress in the biomedical field, particularly in the prediction of drug-drug interactions (DDIs). DDIs are pharmacodynamic reactions between two or more drugs in the body, which may lead to adverse effects ...
Computational Biology Drug Interactions Humans Deep Learning
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GENNDTI: Drug-Target Interaction Prediction Using Graph Neural Network Enhanced by Router Nodes.

IEEE journal of biomedical and health informatics Dec 1, 2024
Identifying drug-target interactions (DTI) is crucial in drug discovery and repurposing, and in silico techniques for DTI predictions are becoming increasingly important for reducing time and cost. Most interaction-based DTI models rely on the guilt-...
Drug Interactions Graph Neural Networks Algorithms Drug Discovery Drug Repositioning Pharmaceutical Preparations Computational Biology Humans Neural Networks, Computer
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Drug-Target Interaction Prediction via Deep Multimodal Graph and Structural Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Drug-target interaction (DTI) prediction speeds up drug repurposing, accelerates drug screening, and reduces drug design timeframe. Previous DTI prediction frameworks lack consideration for the multimodal nature of DTI, advanced feature representatio...
Neural Networks, Computer Machine Learning Deep Learning Proteins Algorithms Humans Drug Repositioning Drug Interactions
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