BiComp-DTA: Drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach. [PDF]
PLoS Computational Biology, 2023 Drug-target binding affinity prediction plays a key role in the early stage of drug discovery. Numerous experimental and data-driven approaches have been developed for predicting drug-target binding affinity.
Mahmood Kalemati +2 more
doaj +3 more sources
Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning [PDF]
Scientific Reports, 2022 Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual drug screening. Most DTI prediction methods cast the problem as a binary classification task to predict if interactions exist or as a regression task to ...
Maha A. Thafar +5 more
doaj +3 more sources
GraphATT-DTA: Attention-Based Novel Representation of Interaction to Predict Drug-Target Binding Affinity [PDF]
Biomedicines, 2022 Drug-target binding affinity (DTA) prediction is an essential step in drug discovery. Drug-target protein binding occurs at specific regions between the protein and drug, rather than the entire protein and drug.
Haelee Bae, Hojung Nam
doaj +4 more sources
SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity [PDF]
BMC BiologyBackground Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research.
Xun Wang +6 more
doaj +3 more sources
Predicting drug-target binding affinity with cross-scale graph contrastive learning. [PDF]
Brief Bioinform, 2023 Abstract Identifying the binding affinity between a drug and its target is essential in drug discovery and repurposing. Numerous computational approaches have been proposed for understanding these interactions. However, most existing methods only utilize either the molecular structure information of drugs and targets or the interaction ...
Wang J, Xiao Y, Shang X, Peng J.
europepmc +3 more sources
Prediction of drug–target binding affinity using similarity-based convolutional neural network [PDF]
Scientific Reports, 2021 Identifying novel drug–target interactions (DTIs) plays an important role in drug discovery. Most of the computational methods developed for predicting DTIs use binary classification, whose goal is to determine whether or not a drug–target (DT) pair ...
Jooyong Shim +3 more
doaj +2 more sources
Hierarchical graph representation learning for the prediction of drug-target binding affinity
Information Sciences, 2022 The identification of drug-target binding affinity (DTA) has attracted increasing attention in the drug discovery process due to the more specific interpretation than binary interaction prediction. Recently, numerous deep learning-based computational methods have been proposed to predict the binding affinities between drugs and targets benefiting from ...
Haitao Fu, Shichao Liu
exaly +4 more sources
DeepDTA: deep drug-target binding affinity prediction. [PDF]
Bioinformatics, 2018 Abstract Motivation The identification of novel drug–target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT ...
Öztürk H, Özgür A, Ozkirimli E.
europepmc +5 more sources
BMC BioinformaticsBackground Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target.
Xin Zeng +5 more
doaj +2 more sources
DTITR: End-to-end drug–target binding affinity prediction with transformers
Computers in Biology and Medicine, 2022 The accurate identification of Drug-Target Interactions (DTIs) remains a critical turning point in drug discovery and understanding of the binding process. Despite recent advances in computational solutions to overcome the challenges of in vitro and in vivo experiments, most of the proposed in silico-based methods still focus on binary classification ...
Nelson R C Monteiro +2 more
exaly +3 more sources