Results 231 to 240 of about 159,394 (268)

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

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 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 graph representation. In this paper, we present a deep-learning-based DTA prediction method called N-
Lok-In, Tsui, Te-Cheng, Hsu, Che, Lin
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Predicting Drug-Target Affinity by Learning Protein Knowledge From Biological Networks

IEEE Journal of Biomedical and Health Informatics, 2023
Predicting drug-target affinity (DTA) is a crucial step in the process of drug discovery. Efficient and accurate prediction of DTA would greatly reduce the time and economic cost of new drug development, which has encouraged the emergence of a large number of deep learning-based DTA prediction methods. In terms of the representation of target proteins,
Wenjian Ma, Shugang Zhang, Zhen Li, Mingjian Jiang, Shuang Wang, Nianfan Guo, Yuanfei Li, Xiangpeng Bi, Huasen Jiang, Zhiqiang Wei   +9 more
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Breaking the barriers of data scarcity in drug–target affinity prediction

Briefings in Bioinformatics, 2023
Abstract Accurate prediction of drug–target affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive,
Qizhi Pei, Lijun Wu, Jinhua Zhu, Yingce Xia, Shufang Xie, Tao Qin, Haiguang Liu, Tie-Yan Liu, Rui Yan   +8 more
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Improving drug-target affinity prediction via feature fusion and knowledge distillation

Briefings in Bioinformatics, 2023
Abstract Rapid and accurate prediction of drug-target affinity can accelerate and improve the drug discovery process. Recent studies show that deep learning models may have the potential to provide fast and accurate drug-target affinity prediction. However, the existing deep learning models still have their own disadvantages that make it
Ruiqiang, Lu, Jun, Wang, Pengyong, Li, Yuquan, Li, Shuoyan, Tan, Yiting, Pan, Huanxiang, Liu, Peng, Gao, Guotong, Xie, Xiaojun, Yao   +9 more
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Drug-target Affinity Prediction by Molecule Secondary Structure Representation Network

Current Medicinal Chemistry
Introduction: Identification of drug-target interactions (DTI) is a crucial step in drug development with high specificity and low toxicity. To accelerate the process, computer-aided DTI prediction algorithms have been used to screen compounds or targets rapidly. Furthermore, DTI prediction can be used to identify potential targets for existing drugs,
Yuewei, Tang, Yunhai, Li, Pengpai, Li, Zhi-Ping, Liu   +3 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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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 protein and drug features, leading to improved DTA prediction performance. However, existing methods
Hengliang Guo, Congxiang Zhang, Jiandong Shang, Dujuan Zhang, Yang Guo, Kang Gao, Kecheng Yang, Xu Gao, Dezhong Yao, Wanting Chen, Mengfan Yan, Gang Wu   +11 more
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GEFA: EARLY FUSION APPROACH IN DRUG-TARGET AFFINITY PREDICTION

Journal of Engineering Sciences
Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA) problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues.
null B.VASANTHA, SINGI BHANU SRI, SOMISHETTY DEDEEPYA, POTHEPANGARI DEEPTHI   +3 more
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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
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