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
doaj +4 more sources
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
doaj +4 more sources
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 +4 more sources
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
doaj +3 more sources
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
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
doaj +3 more sources
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
doaj +2 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 +3 more sources
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
Prediction of Drug-Target Affinity Using Attention Neural Network. [PDF]
Int J Mol SciStudying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational
Tang X, Lei X, Zhang Y.
europepmc +3 more sources