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Drug Interactions

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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...

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9.

PLoS computational biology
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...

An AI-based Prediction Model for Drug-drug Interactions in Osteoporosis and Paget's Diseases from SMILES.

Molecular informatics
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...

Graph neural network approaches for drug-target interactions.

Current opinion in structural biology
Developing new drugs remains prohibitively expensive, time-consuming, and often involves safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug discovery process and thus facilitate drug development. Non-Euclidian da...

Prediction of Drug-Target Interactions Based on Network Representation Learning and Ensemble Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Identifying interactions between drugs and target proteins is a critical step in the drug development process, as it helps identify new targets for drugs and accelerate drug development. The number of known drug-protein interactions (positive samples...