Deep-Learning-Based Drug-Target Interaction Prediction.

By Ming Wen, Zhimin Zhang, S. Niu, Haozhi Sha, Rui Yang, Yong-Huan Yun, Hongmei Lu. 2017

505 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

MolTrans: Molecular Interaction Transformer for drug–target interaction prediction (2020, methodology): “] and matrix factorization [14] methods exploit known DTI’s drug-target similarity information and infer new ones. However, these methods are shown to be not generalizable to dierent protein classes [4]. Feature-based methods feed numerical descriptors of drug and proteins into downstream prediction”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, background): “In recently published works [ 116–122], methods such as deep belief neural networks [118, 119], convolutional neural networks [120, 122] and multiple layer perceptrons [121, 122] were used to establish DTI prediction programs.”
Deep learning in drug discovery: an integrative review and future challenges (2022, methodology): “used ECFPs and protein sequence composition descriptors as examples of common and basic features and trained them using semi-supervised learning via a deep belief network (Wen et al. 2017).”
Interpretable bilinear attention network with domain adaptation improves drug–target prediction (2022, unknown): “Therefore, in silico approaches have gained increasing attention and made much progress in the last few years 5,6 .”
Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2015, methodology): “The feedforward DNNs were recently successfully applied to prediction the various drug properties, such as pharmacological action [33, 34], and toxicity [35].”
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (2018, methodology): “However, similarity-based methods are not commonly used at present to predict DTIs, as researchers have found that similarity-based methods work well for DTIs within specific protein classes but not for other classes [7].”
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases (2018, methodology): “In one of the earliest applications of DNNs for DTI prediction, restricted Boltzmann machines (RBMs), which is a two-layer undirected graphical model was employed [248].”
Interpretable Drug Target Prediction Using Deep Neural Representation (2018, methodology): “Instead of using RBM and DBN, [Chan et al. , 2016; Wang et al. , 2018] used stacked Autoencoder for representation learning and SVM or rotation forest for classi-ﬁcation.”
Pre-training Molecular Graph Representation with 3D Geometry (2021, unknown): “19 [96] Ming Wen, Zhimin Zhang, Shaoyu Niu, Haozhi Sha, Ruihan Yang, Yonghuan Yun, and Hongmei Lu.”
Predicting drug–protein interaction using quasi-visual question answering system (2019, methodology): “, 2016]; 2) DBN [Wen et al., 2017], a deep learning method using middle-level features from predefined molecular fingerprints and protein descriptors; 3) E2E [Gao et al.”
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, background): “he Tox21 and SIDER collections, one-shot learning methods strongly dominate simpler machine learning baselines, indicating the potential for strong performance on small biological data sets. DeepDTIs [120] https://github.com/Bjoux2/ DeepDTIs_DBN Training drug-target space extracted from DrugBank con”
DeepDTA: deep drug–target binding affinity prediction (2018, background): “A few studies employing Deep Neural Networks (DNN) have already been proposed for DTI binary class prediction using different input models for proteins and drugs [10,26,49] as well as the ones that employ stacked auto-encoders [52] and deep-belief networks [53].”
Deep learning in cancer diagnosis, prognosis and treatment selection (2021, methodology): “Most common applications include therapy response and resistance (e.g. Dr.VAE [158] or CDRscan [74]), drug combination synergy (e.g. DeepSynergy [75] and Jiang et al. [76]), drug repositioning (e.g. deepDR [77]) and drug-target interactions (e.g. DeepDTI [78]).”