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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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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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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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
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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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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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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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A meta learning and task adaptive approach for drug target affinity prediction [PDF]
Nature CommunicationsAccurate and robust prediction of drug-target affinity (DTA) plays a critical role in drug discovery. While deep learning has advanced DTA prediction, existing methods struggle with limited training data and poor generalization. In this study, we propose
Mengxuan Wan +7 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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