AIMC Topic: Drug Interactions

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TP-DDI: A Two-Pathway Deep Neural Network for Drug-Drug Interaction Prediction.

Interdisciplinary sciences, computational life sciences May 27, 2022
Adverse drug-drug interactions (DDIs) can severely damage the body. Thus, it is essential to accurately predict DDIs. DDIs are complex processes in which many factors can cause interactions. Rather than merely considering one or two of the factors, w...
Neural Networks, Computer Drug Interactions
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HGDTI: predicting drug-target interaction by using information aggregation based on heterogeneous graph neural network.

BMC bioinformatics Apr 12, 2022
BACKGROUND: In research on new drug discovery, the traditional wet experiment has a long period. Predicting drug-target interaction (DTI) in silico can greatly narrow the scope of search of candidate medications. Excellent algorithm model may be more...
Computational Biology Drug Development Algorithms Proteins Drug Interactions Neural Networks, Computer
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Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications.

PeerJ Apr 4, 2022
Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in struc...
Drug Interactions Supervised Machine Learning Machine Learning Pattern Recognition, Automated Databases, Factual
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Evaluation of the pharmacokinetic drug-drug interaction between the antiretroviral agents fostemsavir and maraviroc: a single-sequence crossover study in healthy participants.

HIV research & clinical practice Mar 14, 2022
BACKGROUND: Fostemsavir is an oral prodrug of temsavir, a first-in-class attachment inhibitor that binds HIV-1 gp120, preventing initial HIV attachment and entry into host immune cells.
Piperazines Maraviroc Healthy Volunteers Adult Drug Interactions Organophosphates Humans Anti-Retroviral Agents Anti-HIV Agents Cross-Over Studies
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CNN-DDI: a learning-based method for predicting drug-drug interactions using convolution neural networks.

BMC bioinformatics Mar 7, 2022
BACKGROUND: Drug-drug interactions (DDIs) are the reactions between drugs. They are compartmentalized into three types: synergistic, antagonistic and no reaction. As a rapidly developing technology, predicting DDIs-associated events is getting more a...
Research Design Drug Development Drug Interactions Neural Networks, Computer Algorithms
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DREAM: Drug-drug interaction extraction with enhanced dependency graph and attention mechanism.

Methods (San Diego, Calif.) Feb 15, 2022
Drug-drug interactions (DDIs) aim at describing the effect relations produced by a combination of two or more drugs. It is an important semantic processing task in the field of bioinformatics such as pharmacovigilance and clinical research. Recently,...
Drug Interactions Data Mining Neural Networks, Computer Computational Biology Semantics
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Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2022
The computational prediction of novel drug-target interactions (DTIs) may effectively speed up the process of drug repositioning and reduce its costs. Most previous methods integrated multiple kinds of connections about drugs and targets by construct...
Drug Interactions Drug Repositioning Pharmaceutical Preparations Neural Networks, Computer
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Predicting Drug-Drug Interactions Based on Integrated Similarity and Semi-Supervised Learning.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2022
A drug-drug interaction (DDI) is defined as an association between two drugs where the pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually improve the therapeutic effects of patients, but negative DDIs cause th...
Drug Interactions Supervised Machine Learning Pharmaceutical Preparations Humans Algorithms Least-Squares Analysis
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Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9.

PLoS computational biology Jan 26, 2022
Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic dru...
Computational Biology Drug Interactions Cytochrome P-450 CYP2C9 Cytochrome P-450 Enzyme Inhibitors Humans Machine Learning
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An AI-based Prediction Model for Drug-drug Interactions in Osteoporosis and Paget's Diseases from SMILES.

Molecular informatics Jan 22, 2022
The skeleton is one of the most important organs in the human body in assisting our motion and activities; however, bone density attenuates gradually as we age. Among common bone diseases are osteoporosis and Paget's, two of the most frequently found...
Algorithms Aged Osteoporosis Humans Drug Interactions Artificial Intelligence Machine Learning
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