2020
DOI: 10.1093/bioinformatics/btaa921
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GraphDTA: predicting drug–target binding affinity with graph neural networks

Abstract: The development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug–target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task.… Show more

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Cited by 506 publications
(446 citation statements)
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“…To demonstrate the use of DeepPurpose, we compare DeepPurpose with KronRLS ( Pahikkala et al , 2015 ), a popular DTI method, and GraphDTA ( Nguyen et al , 2020 ) and DeepDTA ( Öztürk et al , 2018 ), state-of-the-art DL methods. We find that many DeepPurpose models achieve comparable prediction performance on two benchmark datasets, DAVIS ( Davis et al , 2011 ) and KIBA ( He et al , 2017 ) ( Fig.…”
Section: Using Deeppurpose For Dti Predictionmentioning
confidence: 99%
See 1 more Smart Citation
“…To demonstrate the use of DeepPurpose, we compare DeepPurpose with KronRLS ( Pahikkala et al , 2015 ), a popular DTI method, and GraphDTA ( Nguyen et al , 2020 ) and DeepDTA ( Öztürk et al , 2018 ), state-of-the-art DL methods. We find that many DeepPurpose models achieve comparable prediction performance on two benchmark datasets, DAVIS ( Davis et al , 2011 ) and KIBA ( He et al , 2017 ) ( Fig.…”
Section: Using Deeppurpose For Dti Predictionmentioning
confidence: 99%
“…Deep learning (DL) has advanced traditional computational modeling of compounds by offering an increased expressive power in identifying, processing and extrapolating complex patterns in molecular data ( Lee et al , 2019 ; Öztürk et al , 2018 ). There are many DL models designed for DTI prediction (Lee et al , 2019; Nguyen et al , 2020 ; Öztürk et al , 2018 ). However, to generate predictions, deploy DL models in practice, test and evaluate model performance, one needs considerable programming skills and extensive biochemical knowledge.…”
Section: Introductionmentioning
confidence: 99%
“…This study realized the prediction of the real-valued interaction strength between drugs and targets and solved the cold-target problem. There are also some studies similar to the general thinking of the above-mentioned method but differ in data processing ( Gao et al, 2018 ; Nguyen et al, 2021 ). Miyazaki et al (2020) provided a drug–target interaction prediction model that ligands were specifically targeting toward proteins without using true negative interaction information.…”
Section: Typical Application Of Gnns In Bioinformaticsmentioning
confidence: 99%
“…End-to-end deep learning approaches have recently gained momentum. 10 11 Various neural network architectures, including Convolutional Neural Network (CNN), 12 13 seq2seq, 14 15 and Transformer, 16 have been applied to represent protein sequences. These works mainly focused on filling in missing CPIs for the existing drug targets.…”
Section: Introductionmentioning
confidence: 99%