Improving drug–target affinity prediction by adaptive self-supervised learning [PDF]
PeerJ Computer ScienceComputational drug-target affinity prediction is important for drug screening and discovery. Currently, self-supervised learning methods face two major challenges in drug-target affinity prediction.
Qing Ye, Yaxin Sun
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Learnable protein representations in computational biology for predicting drug-target affinity [PDF]
Journal of CheminformaticsIn this review, we discuss the various different types of learnable protein representations that have been used in computational biology, with a particular focus on representations that have been used in the paradigm of predicting drug-target affinity ...
Rachit Kumar +2 more
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Graph neural pre-training based drug-target affinity prediction [PDF]
Frontiers in GeneticsComputational drug-target affinity prediction has the potential to accelerate drug discovery. Currently, pre-training models have achieved significant success in various fields due to their ability to train the model using vast amounts of unlabeled data.
Qing Ye, Yaxin Sun, Yaxin Sun
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Drug–target affinity prediction with extended graph learning-convolutional networks [PDF]
BMC BioinformaticsBackground High-performance computing plays a pivotal role in computer-aided drug design, a field that holds significant promise in pharmaceutical research.
Haiou Qi, Ting Yu, Wenwen Yu, Chenxi Liu
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A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning [PDF]
Frontiers in PharmacologyAccurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing.
Xin Zeng +4 more
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A geometric graph-based deep learning model for drug-target affinity prediction [PDF]
BMC BioinformaticsIn structure-based drug design, accurately estimating the binding affinity between a candidate ligand and its protein receptor is a central challenge.
Md Masud Rana +2 more
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Explainable deep drug–target representations for binding affinity prediction
BMC Bioinformatics, 2022 Background Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug–target interactions and new leads. However, most of these methodologies have been overlooking the importance of providing
Nelson R. C. Monteiro +5 more
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GANsDTA: Predicting Drug-Target Binding Affinity Using GANs
Frontiers in Genetics, 2020 The computational prediction of interactions between drugs and targets is a standing challenge in drug discovery. State-of-the-art methods for drug-target interaction prediction are primarily based on supervised machine learning with known label ...
Lingling Zhao +4 more
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GEFA: Early Fusion Approach in Drug-Target Affinity Prediction [PDF]
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022 Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA) problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues.
Tri Minh Nguyen +2 more
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Graph-sequence attention and transformer for predicting drug-target affinity. [PDF]
RSC Adv, 2022 We proposed a novel model based on self-attention, called GSATDTA, to predict the binding affinity between drugs and targets. Experimental results show that our model outperforms the state-of-the-art methods on two independent datasets.
Yan X, Liu Y.
europepmc +3 more sources