Results 101 to 110 of about 5,508,972 (143)

Parameter-Efficient Adaptation of Large Language Models for Drug-Target Affinity Modeling in Drug Discovery

Accurate prediction of protein ligand binding affinity is crucial for selection of promising hit compounds with higher likelihood of target engagement in drug discovery and development.
Kaira, Virendra Singh
core   +1 more source

Impact of Protein Representations on Drug-Target Affinity Prediction

and target proteins can significantly hasten the drug discovery and development process. Utilizing artificial intelligence (AI) models to predict drug-target affinity (DTA) is an affordable and efficient strategy for sifting out undesirable molecules ...
Marijan, Matija, Tanasijević, Ivan
core  

SMT-DTA: Improving Drug-Target Affinity Prediction with Semi-supervised Multi-task Training

arXiv.org, 2022
Qizhi Pei, Lijun Wu, Jinhua Zhu, Ying-Ce Xia, Shufang Xie, Tao Qin, Hai-Guang Liu, Tie-Yan Liu   +7 more
semanticscholar   +1 more source

ImageDTA: A Simple Model for Drug–Target Binding Affinity Prediction

ACS Omega
Li Han, Ling Kang, Quan Guo
doaj   +1 more source

TAG-DTA: Binding-region-guided strategy to predict drug-target affinity using transformers

Expert systems with applications
Nelson R. C. Monteiro, J. L. Oliveira, Joel P. Arrais   +2 more
semanticscholar   +1 more source

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Drug-target affinity prediction method based on multi-scale information interaction and graph optimization

Comput. Biol. Medicine, 2023
Drug-target affinity (DTA) prediction as an emerging and effective method is widely applied to explore the strength of drug-target interactions in drug development research.
Zhi-Qin Zhu, Zheng Yao, Xin Zheng, Guanqiu Qi, Yuanyuan Li, Neal Mazur, Xin-Bo Gao, Yifei Gong, Bai-Sen Cong   +8 more
semanticscholar   +1 more source

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 ...
Wen-Jian Ma, Shu-Gang Zhang, Zhuguo Li, Mingjian Jiang, Shuang Wang, Nianfa Guo, Yuanfei Li, Xiang-Peng Bi, Hua-Sen Jiang, Zhi-Qiang Wei   +9 more
semanticscholar   +1 more source

HiSIF-DTA: A Hierarchical Semantic Information Fusion Framework for Drug-Target Affinity Prediction

IEEE journal of biomedical and health informatics, 2023
Accurately identifying drug-target affinity (DTA) plays a significant role in promoting drug discovery and has attracted increasing attention in recent years.
Xiang-Peng Bi, Shu-Gang Zhang, Wen-Jian Ma, Hua-Sen Jiang, Zhi-Qiang Wei   +4 more
semanticscholar   +1 more source

MMDTA: A Multimodal Deep Model for Drug-Target Affinity with a Hybrid Fusion Strategy

Journal of Chemical Information and Modeling, 2023
The prediction of the drug-target affinity (DTA) plays an important role in evaluating molecular druggability. Although deep learning-based models for DTA prediction have been extensively attempted, there are rare reports on multimodal models that ...
Kailun Zhong, Meng-Liang Wen, Fan-Fang Meng, Xin Li, Bei Jiang, Xin Zeng, Yi Li   +6 more
semanticscholar   +1 more source

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

IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2022
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 ...
Xixi Yang, Zhangming Niu, Yuansheng Liu, Bosheng Song, Weiqiang Lu, Li Zeng, Xiangxiang Zeng   +6 more
semanticscholar   +1 more source