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GraphDTA: prediction of drug-target binding affinity using graph convolutional networks [article]

Thin Nguyen, Hang Le, Svetha Venkatesh
<span title="2019-06-27">2019</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/684662">doi:10.1101/684662</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hegh7wo26zalzb5nz422hnir5y">fatcat:hegh7wo26zalzb5nz422hnir5y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200310051720/https://www.biorxiv.org/content/biorxiv/early/2019/08/21/684662.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f7/e8/f7e8746340b4331c654993db8b7d7bf8d77f1233.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/684662"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

DeepNC: a framework for drug-target interaction prediction with graph neural networks

Huu Ngoc Tran Tran, J. Joshua Thomas, Nurul Hashimah Ahamed Hassain Malim
<span title="2022-05-11">2022</span> <i title="PeerJ"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eyfkjqp7sva5bbnwatk5zazi7q" style="color: black;">PeerJ</a> </i> &nbsp;
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  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.7717/peerj.13163">doi:10.7717/peerj.13163</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35578674">pmid:35578674</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC9107302/">pmcid:PMC9107302</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h2fdc746xfhi7cpenmnnhjkuxm">fatcat:h2fdc746xfhi7cpenmnnhjkuxm</a> </span>
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Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction

Xianfang Wang, Yifeng Liu, Fan Lu, Hongfei Li, Peng Gao, Dongqing Wei
<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> </i> &nbsp;
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fbioe.2020.00267">doi:10.3389/fbioe.2020.00267</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32318557">pmid:32318557</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7147459/">pmcid:PMC7147459</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d5bq3hceibb4hba76wgx2em7e4">fatcat:d5bq3hceibb4hba76wgx2em7e4</a> </span>
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MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction

Ziduo Yang, Weihe Zhong, Lu Zhao, Calvin Yu-Chian Chen
<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> </i> &nbsp;
MGraphDTA is designed to capture the local and global structure of a compound simultaneously for drugtarget 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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1039/d1sc05180f">doi:10.1039/d1sc05180f</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35173947">pmid:35173947</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8768884/">pmcid:PMC8768884</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3azpqifhavbothgkm7mimjj3gy">fatcat:3azpqifhavbothgkm7mimjj3gy</a> </span>
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Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction [article]

Jingbo Zhou, Shuangli Li, Liang Huang, Haoyi Xiong, Fan Wang, Tong Xu, Hui Xiong, Dejing Dou
<span title="2020-12-17">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.09624v1">arXiv:2012.09624v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/27kl2plyfbbdvbt7ro7xclg5ty">fatcat:27kl2plyfbbdvbt7ro7xclg5ty</a> </span>
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An Interpretable Framework for Drug-Target Interaction with Gated Cross Attention [article]

Yeachan Kim, Bonggun Shin
<span title="2021-09-17">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.08360v1">arXiv:2109.08360v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lbq5hbngxjdwllz2nogfe5uoj4">fatcat:lbq5hbngxjdwllz2nogfe5uoj4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211008080813/https://arxiv.org/pdf/2109.08360v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/75/2f/752f78588f0b015b3de2ba78d8b7d75a72c02cb2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.08360v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRU [article]

Lyu Zhijian, Jiang Shaohua, Liang Yigao, Gao Min
<span title="2022-04-25">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</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  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.11857v1">arXiv:2204.11857v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/i4bvbfgpe5bf5nypuhtq6izs6q">fatcat:i4bvbfgpe5bf5nypuhtq6izs6q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220427005220/https://aircconline.com/csit/papers/vol12/csit120703.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f4/f2/f4f261968f646a69de9331724608ebea753dda48.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.11857v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction

Yuan Jin, Jiarui Lu, Runhan Shi, Yang Yang
<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> </i> &nbsp;
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] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/biom11121783">doi:10.3390/biom11121783</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34944427">pmid:34944427</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8698792/">pmcid:PMC8698792</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/22xg5q4fl5eq3lebgqvsgk5n54">fatcat:22xg5q4fl5eq3lebgqvsgk5n54</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220505000435/https://mdpi-res.com/d_attachment/biomolecules/biomolecules-11-01783/article_deploy/biomolecules-11-01783-v2.pdf?version=1638335422" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/99/8e/998ee3fc004dbdf64060938fe66b7168aaed75d0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/biom11121783"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8698792" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Drug–target affinity prediction using graph neural network and contact maps

Mingjian Jiang, Zhen Li, Shugang Zhang, Shuang Wang, Xiaofeng Wang, Qing Yuan, Zhiqiang Wei
<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> </i> &nbsp;
Prediction of drugtarget 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 classication model to predict whether a drug can interact  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1039/d0ra02297g">doi:10.1039/d0ra02297g</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35517730">pmid:35517730</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC9054320/">pmcid:PMC9054320</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s2vilshpujglraa4avlqco2a5i">fatcat:s2vilshpujglraa4avlqco2a5i</a> </span>
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Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity [article]

Shuangli Li, Jingbo Zhou, Tong Xu, Liang Huang, Fan Wang, Haoyi Xiong, Weili Huang, Dejing Dou, Hui Xiong
<span title="2021-07-21">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.10670v1">arXiv:2107.10670v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iimbz254xzhwtf3h6etofty4a4">fatcat:iimbz254xzhwtf3h6etofty4a4</a> </span>
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HybridDTA: Hybrid Data Fusion through Pairwise Training for Drug-Target Affinity Prediction [article]

Hongyu Luo, Yingfei Xiang, Xiaomin Fang, Wei Lin, Fan Wang, Hua Wu, Haifeng Wang
<span title="2021-11-23">2021</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2021.11.23.469641">doi:10.1101/2021.11.23.469641</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6k6v5ispcrewta54rc2ookeqsy">fatcat:6k6v5ispcrewta54rc2ookeqsy</a> </span>
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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

Xun Wang, Dayan Liu, Jinfu Zhu, Alfonso Rodriguez-Paton, Tao Song
<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> </i> &nbsp;
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  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/biom11050643">doi:10.3390/biom11050643</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33925310">pmid:33925310</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gppeiqxyenhyjmny7j7jcnuzky">fatcat:gppeiqxyenhyjmny7j7jcnuzky</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210502145229/https://res.mdpi.com/d_attachment/biomolecules/biomolecules-11-00643/article_deploy/biomolecules-11-00643.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e1/3c/e13c46002527ea7b12ba976ab6bdbd65d7983d43.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/biom11050643"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

X-DPI: A structure-aware multi-modal deep learning model for drug-protein interactions prediction [article]

Penglei Wang, Shuangjia Zheng, Yize Jiang, Chengtao Li, Junhong Liu, Chang Wen, Atanas Patronov, Dahong Qian, Hongming Chen, Yuedong Yang
<span title="2021-06-18">2021</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2021.06.17.448780">doi:10.1101/2021.06.17.448780</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/imttzr64cvdw5lec2enomcptbm">fatcat:imttzr64cvdw5lec2enomcptbm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210717144258/https://www.biorxiv.org/content/biorxiv/early/2021/06/18/2021.06.17.448780.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3f/2d/3f2dfe41817fb7288622bdfaf75be4d0f4f88ffc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2021.06.17.448780"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Associative Learning Mechanism for Drug-Target Interaction Prediction [article]

Zhiqin Zhu, Zheng Yao, Guanqiu Qi, Neal Mazur, Baisen Cong
<span title="2022-06-01">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.15364v2">arXiv:2205.15364v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d6xtkobljbgb7nc5uyfxoyxzlm">fatcat:d6xtkobljbgb7nc5uyfxoyxzlm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220611122439/https://arxiv.org/pdf/2205.15364v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/cb/01/cb01d46f20215cb9cb9e5c6314feecc1d2e6a111.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.15364v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Repositioning Drugs to the Mitochondrial Fusion Protein 2 by Three-Tunnel Deep Neural Network for Alzheimer's Disease

Xun Wang, Yue Zhong, Mao Ding
<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> </i> &nbsp;
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.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fgene.2021.638330">doi:10.3389/fgene.2021.638330</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33659028">pmid:33659028</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7917248/">pmcid:PMC7917248</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/g65gov4trrenxg5vyzaajsyupa">fatcat:g65gov4trrenxg5vyzaajsyupa</a> </span>
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