Results 231 to 240 of about 600,341 (272)

Modality-DTA: Multimodality Fusion Strategy for Drug–Target Affinity Prediction

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023
Prediction of the drug-target affinity (DTA) plays an important role in drug discovery. Existing deep learning methods for DTA prediction typically leverage a single modality, namely simplified molecular input line entry specification (SMILES) or amino acid sequence to learn representations.
Xixi Yang, Zhangming Niu, Yuansheng Liu, Bosheng Song, Weiqiang Lu, Li Zeng, Xiangxiang Zeng   +6 more
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Affinity-Based Methods in Drug-Target Discovery

Current Drug Targets, 2015
Target discovery using the molecular approach, as opposed to the more traditional systems approach requires the study of the cellular or biological process underlying a condition or disease. The approaches that are employed by the "bench" scientist may be genetic, genomic or proteomic and each has its rightful place in the drug-target discovery process.
Gabriela, Rylova, Tomas, Ozdian, Lakshman, Varanasi, Miroslav, Soural, Jan, Hlavac, Dusan, Holub, Petr, Dzubak, Marian, Hajduch   +7 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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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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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
openaire   +2 more sources