Results 231 to 240 of about 517,836 (269)

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

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
Identifying drug-target interactions has been a key step in drug discovery. Many computational methods have been proposed to directly determine whether drugs and targets can interact or not. Drug-target binding affinity is another type of data which could show the strength of the binding interaction between a drug and a target.
Tianjiao Li, Xing-Ming Zhao, Limin Li
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TransVAE-DTA: Transformer and variational autoencoder network for drug-target binding affinity prediction

Computer Methods and Programs in Biomedicine
Recent studies have emphasized the significance of computational in silico drug-target binding affinity (DTA) prediction in the field of drug discovery and drug repurposing. However, existing DTA prediction approaches suffer from two major deficiencies that impede their progress.
Changjian, Zhou, Zhongzheng, Li, Jia, Song, Wensheng, Xiang   +3 more
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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
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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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Modelling Drug-Target Binding Affinity using a BERT based Graph Neural network

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021
Understanding the interactions between novel drugs and target proteins is fundamentally important in disease research as discovering drug-protein interactions can be an exceptionally time-consuming and expensive process. Alternatively, this process can be simulated using modern deep learning methods that have the potential of utilising vast quantities ...
Lennox, Mark, Robertson, Neil, Devereux, Barry   +2 more
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Drug-target continuous binding affinity prediction using multiple sources of information

Expert Systems with Applications, 2021
Abstract Drug-target binding affinity prediction has a significant role in the search for new drugs or novel targets for existing drugs. The vast majority of recent computational approaches, presented for the task of drug-target binding affinity prediction, make use of a single source to measure drug-drug or protein-protein similarities ...
Betsabeh Tanoori, Mansoor Zolghadri Jahromi, Eghbal G. Mansoori   +2 more
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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   +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 Liu, Min Chen   +5 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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