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GraphDTA: prediction of drug-target binding affinity using graph convolutional networks
[article]
<span title="2019-06-27">2019</span>
<i title="Cold Spring Harbor Laboratory">
bioRxiv
</i>
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In particular, unlike competing methods, drugs are represented as graphs and graph convolutional networks are used to learn drug-target binding affinity. ...
This demonstrates the practical advantages of graph-based representation for molecules in providing accurate prediction of drug-target binding affinity. ...
In this paper we propose GraphDTA to predict the drug-target binding affinity. In the model, drugs are represented as graphs where the edges are the bonding of atoms. ...
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DeepNC: a framework for drug-target interaction prediction with graph neural networks
<span title="2022-05-11">2022</span>
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Then, representations of the drugs and targets are fed into fully-connected layers to predict the binding affinity values. ...
The models of DeepNC were evaluated on two benchmarked datasets (Davis, Kiba) and one independently proposed dataset (Allergy) to confirm that they are suitable for predicting the binding affinity of drugs ...
neural networks (RNN) and convolutional neural networks (CNN) for learning representations of compounds and supermolecule targets, and for the prediction of compound-protein affinity; secondly, they expanded ...
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Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction
<span title="2020-04-03">2020</span>
<i title="Frontiers Media SA">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tkuhrcyiufdxtkdmjqvay6f2ua" style="color: black;">Frontiers in Bioengineering and Biotechnology</a>
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Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency encoding and a hybrid graph convolutional network. ...
Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. ...
DW provides theoretical guidance on Drug-Targets. All authors have read and approved the final manuscript. ...
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MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction
<span title="2022-01-05">2022</span>
<i title="Royal Society of Chemistry (RSC)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lnaynun4fzdepmirohmumo7whu" style="color: black;">Chemical Science</a>
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MGraphDTA is designed to capture the local and global structure of a compound simultaneously for drug–target affinity prediction and can provide explanations that are consistent with pharmacologists. ...
network was used to predict binding affinities from the compound and protein descriptors. ...
The MGNN with 27 graph convolutional layers and a multiscale convolutional neural network (MCNN) were used to extract the multiscale features of drug and target, respectively. ...
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Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction
[article]
<span title="2020-12-17">2020</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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To this end, in this paper, we propose a diStance-aware Molecule graph Attention Network (S-MAN) tailored to drug-target binding affinity prediction. ...
Since graph neural networks (GNNs) have demonstrated remarkable success in various graph-related tasks, GNNs have been considered as a promising tool to improve the binding affinity prediction in recent ...
a 3D CNN model designed to learn the spatial structure of protein-ligand complexes for drug-target binding affinity prediction. • GraphDTA [14] is an effective graph neural network model, which introduced ...
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An Interpretable Framework for Drug-Target Interaction with Gated Cross Attention
[article]
<span title="2021-09-17">2021</span>
<i >
arXiv
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In silico prediction of drug-target interactions (DTI) is significant for drug discovery because it can largely reduce timelines and costs in the drug development process. ...
However, they pay little attention to the interpretability of their prediction results and feature-level interactions between a drug and a target. ...
Lastly, the concatenated features of a drug and a target are fed to multilayered feed-forward networks to predict the binding affinity. ...
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GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRU
[article]
<span title="2022-04-25">2022</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2022-04-23">2022</span> <i > Data Mining and Machine Learning </i> <span class="release-stage">unpublished</span> <span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5121/csit.2022.120703">doi:10.5121/csit.2022.120703</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/htkkeqmsljgedn34wcqitne6zm">fatcat:htkkeqmsljgedn34wcqitne6zm</a> </span>
In this work, we propose a novel method called GDGRU-DTA to predict the binding affinity between drugs and targets, which is based on GraphDTA, but we consider that protein sequences are long sequences ...
The work for predicting drug and target affinity(DTA) is crucial for drug development and repurposing. ...
CI is the Concordance Index, which is a measure of whether the order of predicted binding affinity values for two random drug-target pairs is consistent with their true values, which value exceeds 0.8 ...
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EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction
<span title="2021-11-29">2021</span>
<i title="MDPI AG">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/clnmwghhxzd35jr6jihmkh3gju" style="color: black;">Biomolecules</a>
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For drugs, we build two levels of graphs to represent compound structural information, namely the atom graph and substructure graph, and adopt graph convolutional network with an attention module to learn ...
This study proposes a new model called EmbedDTI to enhance the representation of both drugs and target proteins, and improve the performance of DTI prediction. ...
For example, DeepDTA employs a convolutional neural network (CNN) to extract local sequence patterns as a high-level feature representation for drug-target binding affinity prediction [23] . ...
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Drug–target affinity prediction using graph neural network and contact maps
<span title="2020-06-01">2020</span>
<i title="Royal Society of Chemistry (RSC)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/x6heqyfrkfhzlnbbt24hnmjoda" style="color: black;">RSC Advances</a>
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Prediction of drug–target affinity by constructing both molecule and protein graphs. ...
Drug-target affinity (DTA) prediction is an important step in virtual screening, which can quickly match target and drug and speed up the process of drug development. ...
used deep convolutional neural networks to nd a new target of the well-known drug cladribine. 14 DeepDTIs used unsupervised pretraining to build a classication model to predict whether a drug can interact ...
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Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity
[article]
<span title="2021-07-21">2021</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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Drug discovery often relies on the successful prediction of protein-ligand binding affinity. ...
Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning the representations of protein-ligand complexes. ...
In this paper, we also focus on the structure-based prediction of protein-ligand binding affinity with incorporating abundant spatial information. Graph Neural Networks for Drug Discovery. ...
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HybridDTA: Hybrid Data Fusion through Pairwise Training for Drug-Target Affinity Prediction
[article]
<span title="2021-11-23">2021</span>
<i title="Cold Spring Harbor Laboratory">
bioRxiv
</i>
<span class="release-stage" >pre-print</span>
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Estimating drug-target binding affinity (DTA) is crucial for various tasks, including drug design, drug repurposing, and lead optimization. ...
These powerful techniques make it possible to screen a massive amount of potential drugs with limited computation cost. ...
domains, to drug-target affinity (DTA) prediction. ...
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CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction
<span title="2021-04-27">2021</span>
<i title="MDPI AG">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/clnmwghhxzd35jr6jihmkh3gju" style="color: black;">Biomolecules</a>
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The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. ...
(drug-target interactions) prediction methods including DeepConv-DTI, CPI-Prediction, CPI-Prediction+CS, DeepGS and DeepGS+CS. ...
GraphDTA using molecular graphs as the input of graph convolutional neural network [13] ; and DeepPurpose integrating a variety of encoding methods of drug molecules and protein amino acid sequences ...
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X-DPI: A structure-aware multi-modal deep learning model for drug-protein interactions prediction
[article]
<span title="2021-06-18">2021</span>
<i title="Cold Spring Harbor Laboratory">
bioRxiv
</i>
<span class="release-stage" >pre-print</span>
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For informative protein representation, we constructed a structure-aware graph neural network method from the protein sequence by combining predicted contact maps and graph neural networks. ...
Motivation: Identifying the drug-protein interactions (DPIs) is crucial in drug discovery, and a number of machine learning methods have been developed to predict DPIs. ...
Acknowledgments We thank the Galixir team for its support and discussion, and with special thanks to Jixian Zhang, Zixuan Liu and Da Wei for the experimental design discussion and technical support. ...
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Associative Learning Mechanism for Drug-Target Interaction Prediction
[article]
<span title="2022-06-01">2022</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2022-05-24">2022</span>
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Drug-target affinity (DTA), which represents the strength of drug-target interaction (DTI), has played an important role in the DTI prediction task over the past decade. ...
The experimental results confirm mutual transformer-drug target affinity (MT-DTA) achieves better performance than other comparative methods. ...
Considering that the molecular structure may be more in line with the biochemical relation of drug-target pair interactions, GraphDTA [19] introduced graph-based models. ...
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Repositioning Drugs to the Mitochondrial Fusion Protein 2 by Three-Tunnel Deep Neural Network for Alzheimer's Disease
<span title="2021-02-15">2021</span>
<i title="Frontiers Media SA">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r7trx2kj6je5jhtaoy3rztibgy" style="color: black;">Frontiers in Genetics</a>
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In the prediction of drug-target binding affinity values, the accuracy of the model is up to 88.82% and the loss value is 0.172. ...
However, there is no specific drug for Mfn2 regulation. In this study, a three-tunnel deep neural network (3-Tunnel DNN) model is constructed and trained on the extended Davis dataset. ...
The GraphDTA model (Nguyen et al., 2020) uses graph convolution neural network to represent the features of drug molecules. Although its loss value is tiny, the calculation cost is too high. ...
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