MSResG: Using GAE and Residual GCN to Predict Drug–Drug Interactions Based on Multi-source Drug Features

Guo, L. Jay; Lei, Xiujuan; Chen, Ming; Pan, Yi

doi:10.1007/s12539-023-00550-6

Cited by 11 publications

(4 citation statements)

References 52 publications

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“…In DDI data analysis, a fundamental step is to choose an appropriate measurement for calculating the similarity between samples. 46 Several measurements are commonly used in DDIs analysis, such as Jaccard, 47,48 AZZOO, 49,50 and Gaussian 51 distance. However, choosing a fixed measurement is only appropriate for one particular encoding method.…”

Section: Methodsmentioning

confidence: 99%

A low-cost machine learning framework for predicting drug–drug interactions based on fusion of multiple features and a parameter self-tuning strategy

Liang,

Lin,

Tan

et al. 2024

Phys. Chem. Chem. Phys.

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Section: Methodsmentioning

confidence: 99%

A low-cost machine learning framework for predicting drug–drug interactions based on fusion of multiple features and a parameter self-tuning strategy

Liang,

Lin,

Tan

et al. 2024

Phys. Chem. Chem. Phys.

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“…Compared with traditional models, GNNs not only make better use of node characteristics and network structures [7], but also inherit end-to-end learning frameworks from deep learning; In addition, there are powerful computing platforms developed [12]. Until now, many kinds of GNNs have been explored to tackle with the heterogeneous graphs [9], [24]. Spectral convolution based GNNs [25], attention based GNNs [13], meta-path based GNNs [14] have shown their applications on DTIs networks.…”

Section: Related Workmentioning

confidence: 99%

Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

Chen,

Jiang,

Lei

et al. 2024

Chinese J. Elect.

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Drug-target interactions (DTIs) prediction plays an important role in the process of drug discovery. Most computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks (SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI, which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions (DDIs) and protein-protein interactions (PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from DrugBank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

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“…Initial research treated DTI prediction as a binary classification task, solely distinguishing between combination and non-combination categories. Inspired by the methods employed in other association prediction studies in the field of bioinformatics [3][4][5][6][7][8], these methods incorporated pharmacological data of drugs and targets or constructed heterogeneous networks that linked drugs, targets, and other biological entities for prediction [9]. However, these methods incur some degree of information loss, as well as challenges including determining the threshold for combination and non-combination, and the lack of reliable non-combination samples [10].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Drug-Target Affinity Using Attention Neural Network

Tang,

Lei,

Zhang

2024

IJMS

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Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational DTA prediction methods have emerged as indispensable tools for this research. In this article, we propose a novel deep learning algorithm named GRA-DTA, for DTA prediction. Specifically, we introduce Bidirectional Gated Recurrent Unit (BiGRU) combined with a soft attention mechanism to learn target representations. We employ Graph Sample and Aggregate (GraphSAGE) to learn drug representation, especially to distinguish the different features of drug and target representations and their dimensional contributions. We merge drug and target representations by an attention neural network (ANN) to learn drug-target pair representations, which are fed into fully connected layers to yield predictive DTA. The experimental results showed that GRA-DTA achieved mean squared error of 0.142 and 0.225 and concordance index reached 0.897 and 0.890 on the benchmark datasets KIBA and Davis, respectively, surpassing the most state-of-the-art DTA prediction algorithms.

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MSResG: Using GAE and Residual GCN to Predict Drug–Drug Interactions Based on Multi-source Drug Features

Cited by 11 publications

References 52 publications

A low-cost machine learning framework for predicting drug–drug interactions based on fusion of multiple features and a parameter self-tuning strategy

A low-cost machine learning framework for predicting drug–drug interactions based on fusion of multiple features and a parameter self-tuning strategy

Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

Prediction of Drug-Target Affinity Using Attention Neural Network

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