Results 221 to 230 of about 5,903,465 (247)

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.
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AttentionDTA: prediction of drug–target binding affinity using attention model

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019
In bioinformatics, machine learning-based prediction of drug-target interaction (DTI) plays an important role in virtual screening of drug discovery. DTI prediction, which have been treated as a binary classification problem, depends on the concentration of two molecules, the interaction between two molecules, and other factors.
Qichang Zhao, Fen Xiao, Mengyun Yang, Yaohang Li, Jianxin Wang 0001   +4 more
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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
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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
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Prediction of drug-target binding affinity based on deep learning models

Computers in Biology and Medicine
The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery. Computerized virtual screening techniques have been used for DTA prediction, greatly reducing the time and economic costs of drug discovery. However, these techniques have not succeeded in reversing the low success rate of new drug development.
Hao Zhang, Xiaoqian Liu, Wenya Cheng, Tianshi Wang, Yuanyuan Chen   +4 more
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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
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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
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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
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TC-DTA: Predicting Drug-Target Binding Affinity With Transformer and Convolutional Neural Networks

IEEE Transactions on NanoBioscience
Bioinformatics is a rapidly evolving field that applies computational methods to analyze and interpret biological data. A key task in bioinformatics is identifying novel drug-target interactions (DTIs), which plays a crucial role in drug discovery.
Xiwei Tang, Yiqiang Zhou, Mengyun Yang, Wenjun Li   +3 more
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MambaTransDTA: A Hybrid Mamba-Transformer Architecture for Accurate Drug-Target Binding Affinity Prediction

Journal of Chemical Information and Modeling
In recent years, deep learning techniques have made significant advances in drug-target affinity (DTA) prediction. However, existing models still have considerable room for improvement in prediction accuracy, robustness, and generalization ability. To address these challenges, we present a novel hybrid model, MambaTransDTA, which integrates the Mamba ...
Xinpo Lou, Jianxiu Cai, Qidong Liu 0007, Shirley W. I. Siu   +3 more
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