Graph neural network approaches for drug|target interactions

Graph neural network approaches for drug-target interactions

The emerging graph neural network has been rapidly applied to predict DTIs, and proved effective in finding repositioning drugs and accelerating drug discovery.

Graph neural network approaches for drug-target interactions

Developing new drugs remains prohibitively expensive, time-consuming, and often involves safety issues. Accurate prediction of drug-target ...

Prediction of drug-drug interaction events using graph neural ...

... target interaction by a random walk with restart method on an interactome network ... Graph neural networks: A review of methods and applications.

Graph neural network approaches for drug-target interactions.

89 References · Identifying drug-target interactions based on graph convolutional network and deep neural network · A network integration approach for drug- ...

iNGNN-DTI: prediction of drug–target interaction with interpretable ...

We propose an interpretable nested graph neural network for DTI prediction (iNGNN-DTI) using pre-trained molecule and protein models.

a framework for drug-target interaction prediction with graph neural ...

We propose Deep Neural Computation (DeepNC), which is a framework utilizing three GNN algorithms: Generalized Aggregation Networks (GENConv), Graph ...

Predicting Drug–Target Interaction Using a Novel Graph Neural ...

We propose a novel deep learning approach for predicting drug–target interaction using a graph neural network. We introduce a distance-aware ...

Multi-layer graph attention neural networks for accurate drug-target ...

In the crucial process of drug discovery and repurposing, precise prediction of drug-target interactions (DTIs) is paramount.

Knowledge mapping of graph neural networks for drug discovery

The results of the keyword analysis clarified that graph neural network has primarily been applied to drug-target interaction, drug repurposing, ...

Drug–target affinity prediction with extended graph learning ...

A survey of drug–target interaction and affinity prediction methods via graph neural networks. Comput Biol Med. 2023;666:107136. Article ...

A review on graph neural networks for predicting synergistic drug ...

2021) is designed to jointly learn drug-target interactions and drug-drug synergy. ... Graph neural network approaches for drug-target ...

GENNDTI: Drug-target interaction prediction using graph neural ...

Meanwhile, some methods enrich DTI networks by incorporating additional networks like DDI and PPI networks, enriching biological signals to ...

Knowledge Graph Neural Network for Drug-Drug Interaction Prediction

More- over, recent studies also adopted knowledge graph. (KG) for DDI prediction. Yet, this line of methods learn node latent embedding directly, but they are.

Graph Convolutional Neural Networks for Predicting Drug-Target ...

(13) Recent methods have also applied 3DCNNs to protein-ligand interaction prediction. (14,15) These methods define local boxes around pocket- ...

A dual graph neural network for drug–drug interactions prediction ...

However, under the current GNNs structure, the majority of approaches learn drug molecular representation from one-dimensional string or two- ...

an ultrafast drug–target interaction inference method based on ...

2.1 Methods. 2.1.1 Model architecture. GeNNius architecture is composed of a Graph Neural Network (GNN) encoder that generates ...

Graph Convolutional Neural Networks for Predicting Drug-Target ...

2019. TLDR. A novel deep learning approach for predicting drug-target interaction using a graph neural network that extracts the graph feature of ...

Predicting drug–target binding affinity with graph neural networks

DTA prediction may also benefit from adopting methods for predicting drug–target interactions (DTI). Approaches in this line of work include ...

Graph neural network approaches for drug-target interactions - OUCI

Graph neural network approaches for drug-target interactions. https://doi.org/10.1016/j.sbi.2021.102327. Journal: Current Opinion in Structural Biology, 2022 ...

HGDTI: predicting drug–target interaction by using information ...

We have developed a heterogeneous graph neural network model, named as HGDTI, which includes a learning phase of network node embedding and a training phase of ...