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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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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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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SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features [PDF]
BMC Bioinformatics, 2023 Background Drug–target affinity (DTA) prediction is a critical step in the field of drug discovery. In recent years, deep learning-based methods have emerged for DTA prediction.
Shourun Pan +3 more
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EMPDTA: An End-to-End Multimodal Representation Learning Framework with Pocket Online Detection for Drug–Target Affinity Prediction [PDF]
MoleculesAccurately predicting drug–target interactions is a critical yet challenging task in drug discovery. Traditionally, pocket detection and drug–target affinity prediction have been treated as separate aspects of drug–target interaction, with few methods ...
Dingkai Huang, Jiang Xie
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MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction [PDF]
BMC BioinformaticsBackground The development of drug–target binding affinity (DTA) prediction tasks significantly drives the drug discovery process forward. Leveraging the rapid advancement of artificial intelligence, DTA prediction tasks have undergone a transformative ...
Jinghong Sun +4 more
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GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information [PDF]
BiomoleculesDrug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has
Kusal Debnath +2 more
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Sequence-based drug-target affinity prediction using weighted graph neural networks [PDF]
BMC Genomics, 2022 Background Affinity prediction between molecule and protein is an important step of virtual screening, which is usually called drug-target affinity (DTA) prediction. Its accuracy directly influences the progress of drug development.
Mingjian Jiang +5 more
doaj +3 more sources
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 0005 +3 more
semanticscholar +6 more sources
A deep learning method for drug-target affinity prediction based on sequence interaction information mining [PDF]
PeerJ, 2023 Background A critical aspect of in silico drug discovery involves the prediction of drug-target affinity (DTA). Conducting wet lab experiments to determine affinity is both expensive and time-consuming, making it necessary to find alternative approaches.
Mingjian Jiang +4 more
doaj +4 more sources