Results 261 to 270 of about 141,734 (283)

GFLearn: Generalized Feature Learning for Drug-Target Binding Affinity Prediction

IEEE Journal of Biomedical and Health Informatics
Predicting drug-target binding affinity is critical for drug discovery, as it helps identify promising drug candidates and predict their effectiveness. Recent advancements in deep learning have made significant progress in tackling this task. However, existing methods heavily rely on training data, and their performance is often limited when predicting
Zibo Huang, Xinrui Weng, Le Ou-Yang
openaire   +2 more sources

Drug-Target Affinity Prediction Based on Improved GraphDTA

2023 5th International Conference on Robotics and Computer Vision (ICRCV), 2023
Zi Ye, Xiangyang Chen, Leiluo Wang, Shukai Jiang   +3 more
openaire   +1 more source

3DProtDTA: the deep learning model for drug-target affinity prediction based on the residue-level protein graphs

bioRxiv, 2022
T. Voitsitskyi, Roman Stratiichuk, Ihor Koleiev, Leonid Popryho, Zakhar Ostrovsky, Pavel Henitsoi, Ivan Khropachev, Volodymyr Vozniak, Roman Zhytar, Diana Nechepurenko, S. Yesylevskyy, Alan Nafiev, S. Starosyla   +12 more
semanticscholar   +1 more source

LLMDTA: Improving Cold-Start Prediction in Drug-Target Affinity with Biological LLM

IEEE Transactions on Computational Biology and Bioinformatics
Drug-target affinity (DTA) prediction plays a crucial role in accelerating the drug development process. Although deep learning-based models achieve strong performance in benchmark datasets, their predictive accuracy declines sharply in cold-start scenarios, i.e., when encountering drugs or proteins absent from the training set.
Wuguo Tang, Qichang Zhao, Jian-xin Wang 0001   +2 more
openaire   +2 more sources

Co-VAE: Drug-Target Binding Affinity Prediction by Co-Regularized Variational Autoencoders

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
Tianjiao Li, Xing-Ming Zhao, Limin Li
exaly  

Drug-target continuous binding affinity prediction using multiple sources of information

Expert Systems With Applications, 2021
Betsabeh Tanoori, Mansoor Zolghadri Jahromi, Eghbal G Mansoori   +2 more
exaly  

Drug-target Affinity Prediction Based on Graph Transformer and Selfattention Mechanism Kinase-specific Drug-target Affinity Prediction with Graph Transformer and Self-Attention Fusion

Current Computer-Aided Drug Design
Introduction/Objective: Traditional drug discovery methods face efficiency bottlenecks in predicting drug-target binding affinity (DTA), particularly for kinase inhibitor screening. This study proposes GTDDTA-a novel deep learning framework based on graph transformers and self-attention mechanisms-to address ...
Shiqian, Han, Jiahao, Shi, Jun, Wang
openaire   +2 more sources

A survey of drug-target interaction and affinity prediction methods via graph neural networks

Computers in Biology and Medicine, 2023
Na Han, Aqing Yang, Hongmin Cai
exaly  

DHAG-DTA: Dynamic Hierarchical Affinity Graph Model for Drug-Target Binding Affinity Prediction

IEEE Transactions on Computational Biology and Bioinformatics
Computational methods for predicting drug-target binding affinity (DTA) are critical for large-scale screening of prospective therapeutic compounds during drug discovery. Deep neural networks (DNNs) have recently shown significant promise for DTA prediction.
Cheng Wang, Yang Liu, Shitao Song, Kun Cao, Xiaoyan Liu, Gaurav Sharma, Maozu Guo   +6 more
openaire   +2 more sources

Drug-target affinity prediction using rotary encoding and information retention mechanisms

Engineering applications of artificial intelligence
Zhiqin Zhu, Yan Ding, Guanqiu Qi, Baisen Cong, Yuanyuan Li, Litao Bai, Xinbo Gao   +6 more
semanticscholar   +1 more source