AIMC Topic: Drug Interactions

Clear Filters Showing 111 to 120 of 291 articles

Drug-target interaction prediction using reliable negative samples and effective feature selection methods.

Journal of pharmacological and toxicological methods
Machine learning-based approaches in the field of drug discovery have dramatically reduced the time and cost of the laboratory process of detecting potential drug-target interactions (DTIs). Standard binary classifiers require both positive and negat...

Multi-type feature fusion based on graph neural network for drug-drug interaction prediction.

BMC bioinformatics
BACKGROUND: Drug-Drug interactions (DDIs) are a challenging problem in drug research. Drug combination therapy is an effective solution to treat diseases, but it can also cause serious side effects. Therefore, DDIs prediction is critical in pharmacol...

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

Interdisciplinary sciences, computational life sciences
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...

HGDTI: predicting drug-target interaction by using information aggregation based on heterogeneous graph neural network.

BMC bioinformatics
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...

Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications.

PeerJ
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...

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
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.

CNN-DDI: a learning-based method for predicting drug-drug interactions using convolution neural networks.

BMC bioinformatics
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...

DREAM: Drug-drug interaction extraction with enhanced dependency graph and attention mechanism.

Methods (San Diego, Calif.)
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,...

Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions.

IEEE/ACM transactions on computational biology and bioinformatics
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...

Predicting Drug-Drug Interactions Based on Integrated Similarity and Semi-Supervised Learning.

IEEE/ACM transactions on computational biology and bioinformatics
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...