Research graph
References from A brief review of protein–ligand interaction prediction. Local targets link to admitted publications; unresolved targets remain external evidence.
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Identification of drug-side effect association via multiple information integration with centered kernel alignment
10.1016/j.neucom.2018.10.028 · 2019 · External reference
Predicting drug-target interaction network using deep learning model
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10.1021/ci025538y · 2002 · External reference
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10.1016/j.bbrc.2003.08.093 · 2003 · External reference
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10.2174/1389450118666161116130155 · 2017 · External reference
A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network
10.1089/cmb.2017.0135 · 2018 · External reference
Deep learning-based transcriptome data classification for drug-target interaction prediction. Bmc
2018 · External reference
Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity
10.1007/978-1-4939-9752-7_1 · 2019 · External reference
Prediction of drug-target interactions via deep learning with convolution on protein sequences
2019 · External reference
Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
10.1093/bioinformatics/bty535 · 2019 · External reference
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Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach
2018 · External reference
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Exploring Artificial Intelligence in Drug Discovery
2021 · External reference
SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines
2017 · External reference
Simplified molecular input line entry system (SMILES) as an alternative for constructing quantitative structure-property relationships (QSPR)
2005 · External reference
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The molecular structure and biological functions of RNA methylation, with special emphasis on the roles of RNA methylation in autoimmune diseases
2021 · External reference
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10.1016/j.future.2020.08.005 · 2021 · External reference
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AttentionDTA: prediction of drug-target binding affinity using attention model
2019 · External reference
A multi-task neural network for drug-target interaction prediction
2021 · External reference
Multi-PLI: interpretable multi-task deep learning model for unifying protein-ligand interaction datasets
10.1186/s13321-021-00510-6 · 2021 · External reference
Sequence-based prediction of protein binding regions and drug-target interactions
10.1186/s13321-022-00584-w · 2022 · External reference
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Comparing structural fingerprints using a literature-based similarity benchmark
2016 · External reference
DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction
2020 · External reference
10.1101/2021.12.01.470868
10.1101/2021.12.01.470868 · External reference
DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks
10.1093/bioinformatics/btz111 · 2019 · External reference
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Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions
10.1093/bib/bbab476 · 2022 · External reference
Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms. Appl
2019 · External reference
10.1016/j.neucom.2021.03.091
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MolTrans: Molecular Interaction Transformer for drug-target interaction prediction
10.1093/bioinformatics/btaa880 · 2021 · External reference
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10.1109/access.2021.3071274 · 2021 · External reference
TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
10.1093/bioinformatics/btaa524 · 2020 · External reference
GEFA: early fusion approach in drug-target affinity prediction
2021 · External reference
Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction
2021 · External reference
A deep learning model for plant lncRNA-protein interaction prediction with graph attention
10.1007/s00438-020-01682-w · 2020 · External reference
From Chemical Graphs in Computer-Aided Drug Design to General Markov-Galvez Indices of Drug-Target, Proteome, Drug-Parasitic Disease, Technological, and Social-Legal Networks
10.2174/157340911798260340 · 2011 · External reference
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10.1016/j.csbj.2014.11.005 · External reference
MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters
10.1093/bioinformatics/btz016 · 2019 · External reference
Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks
2021 · External reference
A Survey of the Usages of Deep Learning for Natural Language Processing
2021 · External reference
10.1016/j.drudis.2020.01.003
10.1016/j.drudis.2020.01.003 · External reference
AutoDTI plus plus : deep unsupervised learning for DTI prediction by autoencoders. Bmc
2021 · External reference
10.1002/ddr.20418
10.1002/ddr.20418 · External reference
BioERP: biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions
10.1093/bioinformatics/btab565 · 2021 · External reference
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10.1093/bfgp/elab036 · External reference
Inspector: a lysine succinylation predictor based on edited nearest-neighbor undersampling and adaptive synthetic oversampling
2020 · External reference
10.1021/acs.jproteome.6b00618
10.1021/acs.jproteome.6b00618 · External reference
DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches
10.1093/bioinformatics/bty417 · 2018 · External reference
DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features
10.1093/bib/bbz152 · 2021 · External reference
10.1039/c9sc03414e
10.1039/c9sc03414e · External reference
A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network. Bmc
2020 · External reference
CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction
2021 · External reference
GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network
10.3389/fgene.2021.650821 · 2021 · External reference
A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning
2021 · External reference
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction
2021 · External reference
MultiDTI: drug-target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network
10.1093/bioinformatics/btab473 · 2021 · External reference
HyperAttentionDTI: improving drug-protein interaction prediction by sequence-based deep learning with attention mechanism
10.1093/bioinformatics/btab715 · 2022 · External reference
Identifying drug-target interactions via heterogeneous graph attention networks combined with cross-modal similarities
2022 · External reference
HIDTI: integration of heterogeneous information to predict drug-target interactions
10.1038/s41598-022-07608-3 · 2022 · External reference
HGDTI: predicting drug-target interaction by using information aggregation based on heterogeneous graph neural network. Bmc
2022 · External reference
Unresolved reference
External reference
10.1093/bioinformatics/bty593
10.1093/bioinformatics/bty593 · External reference
10.1093/bioinformatics/bty593
10.1093/bioinformatics/bty593 · External reference
10.1093/bioinformatics/btaa921
10.1093/bioinformatics/btaa921 · External reference
Taba: A Tool to Analyze the Binding Affinity
10.1002/jcc.26048 · 2020 · External reference
Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction. Front Bioeng
2020 · External reference
Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS
10.2174/0929867327666200515101820 · 2021 · External reference
DeepCDA: deep cross-domain compound-protein affinity prediction through LSTM and convolutional neural networks
10.1093/bioinformatics/btaa544 · 2020 · External reference
Drug-target affinity prediction using graph neural network and contact maps
10.1039/d0ra02297g · 2020 · External reference
Multi-view self-attention for interpretable drug-target interaction prediction
10.1016/j.jbi.2020.103547 · 2020 · External reference
Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference
10.1021/acs.jcim.0c01306 · 2021 · External reference
DeepDTAF: a deep learning method to predict protein-ligand binding affinity
2021 · External reference
ML-DTI: Mutual Learning Mechanism for Interpretable Drug-Target Interaction Prediction
10.1021/acs.jpclett.1c00867 · 2021 · External reference
S.A.G.-D.T.A. Prediction of Drug-Target Affinity Using Self-Attention Graph Network
2021 · External reference
Using drug-drug and protein-protein similarities as feature vector for drug-target binding prediction
10.1016/j.chemolab.2021.104405 · 2021 · External reference
10.1137/1.9781611977172.82
10.1137/1.9781611977172.82 · External reference
MGraphDTA: deep multiscale graph neural network for explainable drug-target binding affinity prediction
10.1039/d1sc05180f · 2022 · External reference
FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction
2022 · External reference
ELECTRA-DTA: a new compound-protein binding affinity prediction model based on the contextualized sequence encoding
10.1186/s13321-022-00591-x · 2022 · External reference
