Results 241 to 250 of about 141,734 (283)

G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction

Computers in Biology and Medicine
Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA), indicating the binding capability between drugs and target proteins, is a crucial indicator for drug development.
Xihe Qiu, Haoyu Wang, Xiaoyu Tan
exaly   +2 more sources

Integrating sequence and graph information for enhanced drug-target affinity prediction

Science China Information Sciences
Haohuai He, Guanxing Chen, Calvin Yu-Chian Chen   +2 more
openaire   +2 more sources

Multimodal contrastive representation learning for drug-target binding affinity prediction

Methods, 2023
In the biomedical field, the efficacy of most drugs is demonstrated by their interactions with targets, meanwhile, accurate prediction of the strength of drug-target binding is extremely important for drug development efforts. Traditional bioassay-based drug-target binding affinity (DTA) prediction methods cannot meet the needs of drug R&D in the era ...
Linlin, Zhang, Chunping, Ouyang, Yongbin, Liu, Yiming, Liao, Zheng, Gao   +4 more
openaire   +2 more sources

MLC-DTA: Drug-target affinity prediction based on multi-level contrastive learning and equivariant graph neural networks

Neurocomputing
Guicong Sun, Yongxian Fan
exaly   +2 more sources

MutualDTA: An Interpretable Drug-Target Affinity Prediction Model Leveraging Pretrained Models and Mutual Attention

Journal of Chemical Information and Modeling
Efficient and accurate drug-target affinity (DTA) prediction can significantly accelerate the drug development process. Recently, deep learning models have been widely applied to DTA prediction and have achieved notable success. However, existing methods
Yongna Yuan, Siming Chen, Rizhen Hu, Xin Wang   +3 more
semanticscholar   +1 more source

MMSG-DTA: A Multimodal, Multiscale Model Based on Sequence and Graph Modalities for Drug-Target Affinity Prediction

Journal of Chemical Information and Modeling
Drug-Target Affinity (DTA) prediction is a cornerstone of drug discovery and development, providing critical insights into the intricate interactions between candidate drugs and their biological targets.
Jiahao Xu, Lei Ci, Bo Zhu, Guanhua Zhang, Linhua Jiang, Shixin Ye-Lehmann, Wei Long   +6 more
semanticscholar   +1 more source

Drug-Target Affinity Prediction Based on Topological Enhanced Graph Neural Networks

Journal of Chemical Information and Modeling
Graph neural networks (GNNs) have achieved remarkable success in drug-target affinity (DTA) analysis, reducing the cost of drug development. Unlike traditional one-dimensional (1D) sequence-based methods, GNNs leverage graph structures to capture richer ...
Hengliang Guo, Congxiang Zhang, Jiandong Shang, Dujuan Zhang, Yang Guo, K. Gao, Kecheng Yang, Xu Gao, Dezhong Yao, Wanting Chen, Mengfan Yan, Gang Wu   +11 more
semanticscholar   +1 more source

AGraphDTA: An Efficient Model for Drug-Target Affinity Prediction with Feature Fusion

2023 International Conference on New Trends in Computational Intelligence (NTCI), 2023
It is a crucial task to predict the Drug-target affinity (DTA) in drug discovery. Recently, the application of deep learning shows a significant improvement on DTA prediction.
Donglin Wang, X. Chen, Xin Bao, Kun Zhou
semanticscholar   +1 more source

NG-DTA: Drug-target affinity prediction with n-gram molecular graphs

Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2023
Drug–target affinity (DTA) prediction is crucial to speed up drug development. The advance in deep learning allows accurate DTA prediction. However, most deep learning methods treat protein as a 1D string which is not informative to models compared to a ...
Lok-In Tsui, Te-Cheng Hsu, Che Lin
semanticscholar   +1 more source

XG-DTA: Drug-Target Affinity Prediction Based on Drug Molecular Graph and Protein Sequence combined with XLNet

2023 IEEE International Conference on Medical Artificial Intelligence (MedAI), 2023
Drug-target affinity (DTA) prediction is critical in drug development. Accurate prediction of drug-target interactions can accelerate the development of new drugs and improve drug safety.
Han Zhou, Xiumin Shi, Yuxiang Wang, Ziyang Wen, Jiaqi Peng   +4 more
semanticscholar   +1 more source