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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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
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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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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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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 +3 more sources
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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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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Optimization of drug-target affinity prediction methods through feature processing schemes. [PDF]
Bioinformatics, 2023 AbstractMotivationNumerous high-accuracy drug–target affinity (DTA) prediction models, whose performance is heavily reliant on the drug and target feature information, are developed at the expense of complexity and interpretability. Feature extraction and optimization constitute a critical step that significantly influences the enhancement of model ...
Ru X, Zou Q, Lin C.
europepmc +3 more sources
Structure-free drug–target affinity prediction using protein and molecule language models [PDF]
Journal of CheminformaticsAccurate prediction of drug-target affinity (DTA) is crucial for advancing drug discovery and optimizing experimental processes. Traditional DTA models often rely on handcrafted features or structural data, which can limit their generalizability and ...
Amir Hallaji Bidgoli +2 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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