Results 1 to 10 of about 2,729,950 (336)

Evidential deep learning-based drug-target interaction prediction [PDF]

Nature Communications
Drug-target interaction (DTI) prediction is a crucial component of drug discovery. Recent deep learning methods show great potential in this field but also encounter substantial challenges.
Yanpeng Zhao, Yuting Xing, Yixin Zhang, Yifei Wang, Mengxuan Wan, Duoyun Yi, Chengkun Wu, Shangze Li, Huiyan Xu, Hongyang Zhang, Ziyi Liu, Guowei Zhou, Mengfan Li, Xuanze Wang, Zhengshan Chen, Ruijiang Li, Lianlian Wu, Dongsheng Zhao, Peng Zan, Song He, Xiaochen Bo   +20 more
doaj   +3 more sources

MolTrans: Molecular Interaction Transformer for drug–target interaction prediction [PDF]

Bioinformatics, 2020
Motivation Drug–target interaction (DTI) prediction is a foundational task for in-silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space.
Kexin Huang, Cao Xiao, Lucas Glass, Jimeng Sun   +3 more
semanticscholar   +5 more sources

Associative learning mechanism for drug‐target interaction prediction [PDF]

CAAI Transactions on Intelligence Technology, 2023
As a necessary process of modern drug development, finding a drug compound that can selectively bind to a specific protein is highly challenging and costly.
Zhiqin Zhu, Zheng Yao, Guanqiu Qi, Neal Mazur, Pan Yang, Baisen Cong   +5 more
doaj   +2 more sources

Machine Learning for Drug-Target Interaction Prediction [PDF]

Molecules, 2018
Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery.
Ruolan Chen, Xiangrong Liu, Shuting Jin, Jiawei Lin, Juan Liu   +4 more
doaj   +3 more sources

Drug–Target Interaction Prediction via Dual-Interaction Fusion [PDF]

Molecules
Accurate prediction of drug–target interaction (DTI) is crucial for modern drug discovery. However, experimental assays are costly, and many existing computational models still face challenges in capturing multi-scale features, fusing cross-modal ...
Xingyang Li, Zepeng Li, Bo Wei, Yuni Zeng   +3 more
doaj   +2 more sources

Drug–target interaction prediction via multiple classification strategies [PDF]

BMC Bioinformatics, 2022
Background Computational prediction of the interaction between drugs and protein targets is very important for the new drug discovery, as the experimental determination of drug-target interaction (DTI) is expensive and time-consuming.
Qing Ye, Xiaolong Zhang, Xiaoli Lin
doaj   +3 more sources

Transfer learning for drug–target interaction prediction

Bioinformatics, 2023
Motivation Utilizing AI-driven approaches for drug–target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the use of deep transfer learning for
Alperen Dalkıran, Ahmet Atakan, A. Rifaioglu, M. Martin, R. Atalay, A. Acar, Tunca Dogan, V. Atalay   +7 more
semanticscholar   +5 more sources

A novel method for drug-target interaction prediction based on graph transformers model

BMC Bioinformatics, 2022
Background Drug-target interactions (DTIs) prediction becomes more and more important for accelerating drug research and drug repositioning. Drug-target interaction network is a typical model for DTIs prediction.
Hongmei Wang, Fang Guo, Mengyan Du, Guishen Wang, Chen Cao   +4 more
doaj   +2 more sources

In silico methods for drug-target interaction prediction [PDF]

Cell Reports: Methods
Summary: Drug-target interaction (DTI) prediction is a crucial component of drug discovery. In recent years, in silico approaches have attracted attention for DTI prediction, primarily because of their potential to mitigate the high costs, low success ...
Xiaoqing Ru, Lifeng Xu, Wu Han, Quan Zou
doaj   +2 more sources

Effective drug–target interaction prediction with mutual interaction neural network

Bioinformatics, 2022
Motivation Accurately predicting drug–target interaction (DTI) is a crucial step to drug discovery. Recently, deep learning techniques have been widely used for DTI prediction and achieved significant performance improvement.
Fei Li, Ziqiao Zhang, J. Guan, Shuigeng Zhou   +3 more
semanticscholar   +3 more sources