A novel method for drug-target interaction prediction based on graph transformers model

Hong-mei, Wang; Guo, Fei; Du, Mingde; Wang, Guishen; Cao, Chen

doi:10.1186/s12859-022-04812-w

Cited by 22 publications

(12 citation statements)

References 51 publications

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“…Therefore, the AUC and AUPR are usually adequate metrics for evaluating the performance of a model for DTI prediction [ 40 ]. Many similar studies have used these two metrics to evaluate the performance of methods for predicting DTIs [ 26 , 28 , 41 – 43 ]. As biologists often select drug-target pairs with high prediction scores for subsequent wet experiment validation, the recall rates of the top (5%, 10%, 15%, 20%, and 30%) proportion of candidate targets predicted by the model were selected.…”

Section: Resultsmentioning

confidence: 99%

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

Chen

Zheng

2023

BMC Bioinformatics

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Background Drug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods used for identifying relevant DTIs do not consider the invariance of the nearest neighbour relationships between drugs or targets. In other words, they do not take into account the invariance of the topological relationships between nodes during representation learning. It may limit the performance of the DTI prediction methods. Results Here, we propose a novel graph convolutional autoencoder-based model, named SDGAE, to predict DTIs. As the graph convolutional network cannot handle isolated nodes in a network, a pre-processing step was applied to reduce the number of isolated nodes in the heterogeneous network and facilitate effective exploitation of the graph convolutional network. By maintaining the graph structure during representation learning, the nearest neighbour relationships between nodes in the embedding space remained as close as possible to the original space. Conclusions Overall, we demonstrated that SDGAE can automatically learn more informative and robust feature vectors of drugs and targets, thus exhibiting significantly improved predictive accuracy for DTIs.

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Section: Resultsmentioning

confidence: 99%

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

Chen

Zheng

2023

BMC Bioinformatics

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“…NodeFormer [20] is a graph transformer model known for its all-pair attention mechanism, enabling efficient graph data processing. Traditional graph neural networks propagate signals over a sparse adjacency matrix, while graph transformers [43] can be seen as propagating signals over a densely connected graph with layer-wise edge weights. The latter requires estimation for the N*N attention matrix and feature propagation over such a dense matrix.…”

Section: Graph Transformer (Nodeformer)mentioning

confidence: 99%

Graph Node Classification to Predict Autism Risk in Genes

Bandara,

Riccardi

2024

Genes

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This study explores the genetic risk associations with autism spectrum disorder (ASD) using graph neural networks (GNNs), leveraging the Sfari dataset and protein interaction network (PIN) data. We built a gene network with genes as nodes, chromosome band location as node features, and gene interactions as edges. Graph models were employed to classify the autism risk associated with newly introduced genes (test set). Three classification tasks were undertaken to test the ability of our models: binary risk association, multi-class risk association, and syndromic gene association. We tested graph convolutional networks, Graph Sage, graph transformer, and Multi-Layer Perceptron (Baseline) architectures on this problem. The Graph Sage model consistently outperformed the other models, showcasing its utility in classifying ASD-related genes. Our ablation studies show that the chromosome band location and protein interactions contain useful information for this problem. The models achieved 85.80% accuracy on the binary risk classification, 81.68% accuracy on the multi-class risk classification, and 90.22% on the syndromic classification.

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“…There are many possibilities for implementing an encoder for protein–ligand complexes, including but not limited to models based on 3D-CNN [ 15 , 58 , 77 ], GNN [ 11 , 12 , 78 ] and Transformer [ 10 , 79 , 80 ]. In MBP, we propose a simple and effective shared bottom encoder.…”

Section: Multi-task Bioassay Pre-trainingmentioning

confidence: 99%

Multi-task bioassay pre-training for protein-ligand binding affinity prediction

Yan,

Ye,

Yang

et al. 2023

Briefings in Bioinformatics

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Protein–ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery. Recently, various deep learning-based models predict binding affinity by incorporating the three-dimensional (3D) structure of protein–ligand complexes as input and achieving astounding progress. However, due to the scarcity of high-quality training data, the generalization ability of current models is still limited. Although there is a vast amount of affinity data available in large-scale databases such as ChEMBL, issues such as inconsistent affinity measurement labels (i.e. IC50, Ki, Kd), different experimental conditions, and the lack of available 3D binding structures complicate the development of high-precision affinity prediction models using these data. To address these issues, we (i) propose Multi-task Bioassay Pre-training (MBP), a pre-training framework for structure-based PLBA prediction; (ii) construct a pre-training dataset called ChEMBL-Dock with more than 300k experimentally measured affinity labels and about 2.8M docked 3D structures. By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels. Experiments substantiate the capability of MBP on the structure-based PLBA prediction task. To the best of our knowledge, MBP is the first affinity pre-training model and shows great potential for future development. MBP web-server is now available for free at: https://huggingface.co/spaces/jiaxianustc/mbp.

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A novel method for drug-target interaction prediction based on graph transformers model

Cited by 22 publications

References 51 publications

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

Graph Node Classification to Predict Autism Risk in Genes

Multi-task bioassay pre-training for protein-ligand binding affinity prediction

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