Results 21 to 30 of about 5,903,465 (247)
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 +5 more sources
InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks [PDF]
HeliyonPredicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance.
Mahmood Kalemati +2 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
Deep drug-target binding affinity prediction with multiple attention blocks. [PDF]
Brief Bioinform, 2021 Abstract Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs.
Zeng Y, Chen X, Luo Y, Li X, Peng D.
europepmc +4 more sources
FingerDTA: A Fingerprint-Embedding Framework for Drug-Target Binding Affinity Prediction
Big Data Mining and Analytics, 2023 Many efforts have been exerted toward screening potential drugs for targets, and conducting wet experiments remains a laborious and time-consuming approach.
Xuekai Zhu +5 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 +4 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 ...
Shichao Liu, Xionghui Zhou, Haitao Fu
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 +7 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
ResDTA: Predicting Drug-Target Binding Affinity Using Residual Skip Connections
CoRR, 2023 40 pages, 10 figures, 2 tables.
Partho Ghosh, Md. Aynal Haque
openaire +3 more sources