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Drug Interactions

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iNGNN-DTI: prediction of drug-target interaction with interpretable nested graph neural network and pretrained molecule models.

Bioinformatics (Oxford, England)
MOTIVATION: Drug-target interaction (DTI) prediction aims to identify interactions between drugs and protein targets. Deep learning can automatically learn discriminative features from drug and protein target representations for DTI prediction, but c...

A Novel Deep Learning Model for Drug-drug Interactions.

Current computer-aided drug design
INTRODUCTION: Drug-drug interactions (DDIs) can lead to adverse events and compromised treatment efficacy that emphasize the need for accurate prediction and understanding of these interactions.

Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding.

Current computer-aided drug design
BACKGROUND: In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topol...

DMFDDI: deep multimodal fusion for drug-drug interaction prediction.

Briefings in bioinformatics
Drug combination therapy has gradually become a promising treatment strategy for complex or co-existing diseases. As drug-drug interactions (DDIs) may cause unexpected adverse drug reactions, DDI prediction is an important task in pharmacology and cl...

HTCL-DDI: a hierarchical triple-view contrastive learning framework for drug-drug interaction prediction.

Briefings in bioinformatics
Drug-drug interaction (DDI) prediction can discover potential risks of drug combinations in advance by detecting drug pairs that are likely to interact with each other, sparking an increasing demand for computational methods of DDI prediction. Howeve...

iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network.

Bioinformatics (Oxford, England)
MOTIVATION: The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are pref...

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction.

Briefings in bioinformatics
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect...

MCFF-MTDDI: multi-channel feature fusion for multi-typed drug-drug interaction prediction.

Briefings in bioinformatics
Adverse drug-drug interactions (DDIs) have become an increasingly serious problem in the medical and health system. Recently, the effective application of deep learning and biomedical knowledge graphs (KGs) have improved the DDI prediction performanc...

Attention-based cross domain graph neural network for prediction of drug-drug interactions.

Briefings in bioinformatics
Drug-drug interactions (DDI) may lead to adverse reactions in human body and accurate prediction of DDI can mitigate the medical risk. Currently, most of computer-aided DDI prediction methods construct models based on drug-associated features or DDI ...

Model and Strategy for Predicting and Discovering Drug-Drug Interactions.

Studies in health technology and informatics
Taking several medications at the same time is an increasingly common phenomenon in our society. The combination of drugs is certainly not without risk of potentially dangerous interactions. Taking into account all possible interactions is a very com...