Results 41 to 50 of about 141,734 (283)
Neural Networks, 2023 The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and design. Traditional experiments are very expensive and time-consuming.
Hongjie Wu +7 more
semanticscholar +1 more source
Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring. [PDF]
Brief Bioinform, 2022 Predicting the drug-target interaction is crucial for drug discovery as well as drug repurposing. Machine learning is commonly used in drug-target affinity (DTA) problem.
Nguyen TM, Nguyen T, Tran T.
europepmc +3 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 ...
Hakime Öztürk +2 more
openaire +3 more sources
Deep drug-target binding affinity prediction with multiple attention blocks [PDF]
Briefings in Bioinformatics, 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.
Yuni Zeng +4 more
openaire +2 more sources
Drug target affinity prediction based on multi-scale gated power graph and multi-head linear attention mechanism. [PDF]
PLoS OneFor the purpose of developing new drugs and repositioning existing ones, accurate drug-target affinity (DTA) prediction is essential. While graph neural networks are frequently utilized for DTA prediction, it is difficult for existing single-scale graph ...
Hu S, Hu J, Zhang X, Jin S, Xu X.
europepmc +2 more sources
Multilevel Attention Models for Drug Target Binding Affinity Prediction [PDF]
Neural Processing Letters, 2021 Drug-Target Binding Affinity (DTBA) prediction is one class of Drug-Target Interaction problem (DTI), where the focus is to predict the binding strength of a drug-target pair. Several machine learning approaches have been developed for this purpose. However, almost all rely on the use of increasingly sophisticated inputs to improve the obtained results
openaire +1 more source
Drug-Target Binding Affinity Prediction Using Transformers [PDF]
, 2021 Abstract Drug discovery is generally difficult, expensive, and low success rate. One of the essential steps in the early stages of drug discovery and drug repurposing is identifying drug-target interactions. Binding affinity indicates the strength of drug-target pair interactions.
Mahsa Saadat +3 more
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Graph–sequence attention and transformer for predicting drug–target affinity
RSC Advances, 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.
Xiangfeng Yan, Yong Liu
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Drug-target affinity prediction using applicability domain based on data density
, 2021 In the pursuit of research and development of drug discovery, the computational prediction of the target affinity of a drug candidate is useful for screening compounds at an early stage and for verifying the binding potential to an unknown target.
Shunya, Sugita, Masahito, Ohue
core +1 more source
AffinityVAE: A multi-objective model for protein-ligand affinity prediction and drug design
, 2023 In the prediction of protein-ligand affinity, the traditional methods require a large amount of computing resources, and have certain limitations in predicting and simulating the structural changes.
Yu, X +6 more
core +1 more source