Results 231 to 240 of about 141,734 (283)

A Framework for Improving the Generalizability of Drug–Target Affinity Prediction Models

Journal of Computational Biology, 2023
Statistical models that accurately predict the binding affinity of an input ligand-protein pair can greatly accelerate drug discovery. Such models are trained on available ligand-protein interaction data sets, which may contain biases that lead the predictor models to learn data set-specific, spurious patterns instead of generalizable relationships ...
Riza Özçelik, Alperen Bag, Berk Atil, Melih Barsbey, Arzucan Özgür, Elif Ozkirimli 0001   +5 more
openaire   +3 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

Improving drug-target affinity prediction via feature fusion and knowledge distillation

Briefings in Bioinformatics, 2023
Rapid and accurate prediction of drug-target affinity can accelerate and improve the drug discovery process. Recent studies show that deep learning models may have the potential to provide fast and accurate drug-target affinity prediction.
Ruiqiang Lu, Pengyong Li, Yuquan Li
exaly   +2 more sources

SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network

International Journal of Molecular Sciences, 2021
The prediction of drug–target affinity (DTA) is a crucial step for drug screening and discovery. In this study, a new graph-based prediction model named SAG-DTA (self-attention graph drug–target affinity) was implemented.
Shugang Zhang, Mingjian Jiang, Zhiqiang Wei   +2 more
exaly   +2 more sources

Effective drug-target affinity prediction via generative active learning

Information Sciences
Yuansheng Liu, Zhenran Zhou, Dong-Sheng Cao   +2 more
exaly   +3 more sources

Drug-target affinity prediction method based on multi-scale information interaction and graph optimization

Comput. Biol. Medicine, 2023
Drug-target affinity (DTA) prediction as an emerging and effective method is widely applied to explore the strength of drug-target interactions in drug development research.
Zhiqin Zhu, Zheng Yao, Xin Zheng, Guanqiu Qi, Yuanyuan Li, Neal Mazur, Xinbo Gao, Yifei Gong, Baisen Cong   +8 more
semanticscholar   +1 more source

CKDTA: A chemical knowledge-enhanced framework for drug–target affinity prediction

Journal of Computational Science
Xingran Zhao, Yanbu Guo, Bingyi Wang, Weihua Li 0006   +3 more
openaire   +2 more sources

HiSIF-DTA: A Hierarchical Semantic Information Fusion Framework for Drug-Target Affinity Prediction

IEEE journal of biomedical and health informatics, 2023
Accurately identifying drug-target affinity (DTA) plays a significant role in promoting drug discovery and has attracted increasing attention in recent years.
Xiangpeng Bi, Shugang Zhang, Wenjian Ma, Huasen Jiang, Zhiqiang Wei   +4 more
semanticscholar   +1 more source

MSGNN-DTA: Multi-Scale Topological Feature Fusion Based on Graph Neural Networks for Drug–Target Binding Affinity Prediction

International Journal of Molecular Sciences, 2023
The accurate prediction of drug–target binding affinity (DTA) is an essential step in drug discovery and drug repositioning. Although deep learning methods have been widely adopted for DTA prediction, the complexity of extracting drug and target ...
Shudong Wang, Kuijie Zhang, Yingye Liu   +2 more
exaly   +2 more sources

A Mutual Attention Model for Drug Target Binding Affinity Prediction

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022
Vrious machine learning approaches have been developed for drug-target interaction (DTI) prediction. One class of these approaches, DTBA, is interested in Drug-Target Binding Affinity strength, rather than focusing merely on the presence or absence of interaction. Several machine learning methods have been developed for this purpose.
openaire   +2 more sources