Results 241 to 250 of about 342,422 (269)

Classification prediction of drug target binding affinity based on the MolrProtTrans model

Analytical Biochemistry
Predicting drug-target interactions is essential for virtual drug screening. While many models predict the binding affinity between small molecules and proteins, they often overemphasize molecular features while overlooking important protein characteristics, leading to biased predictions.
Yicun Lin, Yuanfeng Li, Wei Sun, Jian Wang   +3 more
openaire   +2 more sources

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

Contrastive Meta-Learning for Drug-Target Binding Affinity Prediction

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022
Mei Li, Sihan Xu, Xiangrui Cai, Zhong Zhang 0001, Hua Ji   +4 more
openaire   +1 more source

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

Multimodal Drug Target Binding Affinity Prediction Using Graph Local Substructure

IEEE Journal of Biomedical and Health Informatics
Predicting the binding affinity of drug target is essential to reduce drug development costs and cycles. Recently, several deep learning-based methods have been proposed to utilize the structural or sequential information of drugs and targets to predict the drug-target binding affinity (DTA).
Xun Peng, Chunping Ouyang, Yongbin Liu, Ying Yu, Jian K. Liu, Min Chen 0028   +5 more
openaire   +2 more sources

HPDAF: A practical tool for predicting drug-target binding affinity using multimodal features

European Journal of Medicinal Chemistry
Accurate prediction of drug-target binding affinity is crucial for efficient drug discovery and design, enabling researchers to better understand molecular interactions and accelerate the identification of promising drug candidates. Despite recent advances, existing computational methods often face difficulties in effectively combining detailed ...
An, Gong, Bing, Yu, Lekai, Zhang, Anxuan, Jia, Yuyang, Zhan, Yong, Liu, Shuhui, Wu   +6 more
openaire   +2 more sources

ASAP-DTA: Predicting drug-target binding affinity with adaptive structure aware networks

Journal of Bioinformatics and Computational Biology
The prediction of drug-target affinity (DTA) is crucial for efficiently identifying potential targets for drug repurposing, thereby reducing resource wastage. In this paper, we propose a novel graph-based deep learning model for DTA that leverages adaptive structure-aware pooling for graph processing. Our approach integrates a self-attention mechanism
Weibin Ding, Shaohua Jiang, Ting Xu, Zhijian Lyu   +3 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  

Predicting drug–target binding affinity through molecule representation block based on multi-head attention and skip connection

Briefings in Bioinformatics, 2022
Chun-Chun Wang, Xing Chen
exaly  

GPCNDTA: Prediction of drug-target binding affinity through cross-attention networks augmented with graph features and pharmacophores

Computers in Biology and Medicine, 2023
Chun-Chun Wang, Yiwen Zhang, Xing Chen   +2 more
exaly