AIMC Topic: Drug Interactions

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LR-GNN: a graph neural network based on link representation for predicting molecular associations.

Briefings in bioinformatics Jan 17, 2022
In biomedical networks, molecular associations are important to understand biological processes and functions. Many computational methods, such as link prediction methods based on graph neural networks (GNNs), have been successfully applied in discov...
Protein Interaction Domains and Motifs Biomedical Technology Humans Neural Networks, Computer MicroRNAs RNA, Long Noncoding Drug Interactions Deep Learning Cell Communication Computational Biology Algorithms Research Design
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EGFI: drug-drug interaction extraction and generation with fusion of enriched entity and sentence information.

Briefings in bioinformatics Jan 17, 2022
MOTIVATION: The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the l...
Language Natural Language Processing Drug Interactions Semantics Software
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GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug-protein interaction prediction.

Briefings in bioinformatics Jan 17, 2022
MOTIVATION: Identifying proteins that interact with drugs plays an important role in the initial period of developing drugs, which helps to reduce the development cost and time. Recent methods for predicting drug-protein interactions mainly focus on ...
Drug Interactions Neural Networks, Computer Proteins
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MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism.

Briefings in bioinformatics Jan 17, 2022
One of the main problems with the joint use of multiple drugs is that it may cause adverse drug interactions and side effects that damage the body. Therefore, it is important to predict potential drug interactions. However, most of the available pred...
Humans Drug Interactions Drug-Related Side Effects and Adverse Reactions Neural Networks, Computer Software Oligosaccharides
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Drug-target interaction predication via multi-channel graph neural networks.

Briefings in bioinformatics Jan 17, 2022
Drug-target interaction (DTI) is an important step in drug discovery. Although there are many methods for predicting drug targets, these methods have limitations in using discrete or manual feature representations. In recent years, deep learning meth...
Drug Development Drug Delivery Systems Drug Interactions Neural Networks, Computer Drug Discovery
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Machine learning methods, databases and tools for drug combination prediction.

Briefings in bioinformatics Jan 17, 2022
Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In ...
Drug Interactions Databases, Factual Drug Combinations Machine Learning Algorithms
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Predicting Drug-Target Affinity Based on Recurrent Neural Networks and Graph Convolutional Neural Networks.

Combinatorial chemistry & high throughput screening Jan 1, 2022
BACKGROUND: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interac...
Neural Networks, Computer Drug Interactions Drug Development Amino Acid Sequence Molecular Structure
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Comparative analysis of molecular fingerprints in prediction of drug combination effects.

Briefings in bioinformatics Nov 5, 2021
Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computa...
Drug Combinations Humans Regression Analysis Uncertainty Computer Simulation Deep Learning Drug Discovery Cell Line, Tumor Drug Synergism Drug Interactions Datasets as Topic High-Throughput Screening Assays
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SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction.

Briefings in bioinformatics Nov 5, 2021
A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug-drug interactions (DDIs), which can cause serious injuries to the organism. Although several computat...
Structure-Activity Relationship Drug Interactions Software Computational Biology Neural Networks, Computer
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SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization.

Bioinformatics (Oxford, England) Sep 29, 2021
MOTIVATION: Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an o...
Pattern Recognition, Automated Machine Learning Neural Networks, Computer Drug Interactions
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