Results 21 to 30 of about 160,727 (166)

GANsDTA: Predicting Drug-Target Binding Affinity Using GANs

Frontiers in Genetics, 2020
The computational prediction of interactions between drugs and targets is a standing challenge in drug discovery. State-of-the-art methods for drug-target interaction prediction are primarily based on supervised machine learning with known label ...
Lingling Zhao, Junjie Wang, Long Pang, Yang Liu, Jun Zhang   +4 more
doaj   +3 more sources

A meta learning and task adaptive approach for drug target affinity prediction [PDF]

Nature Communications
Accurate and robust prediction of drug-target affinity (DTA) plays a critical role in drug discovery. While deep learning has advanced DTA prediction, existing methods struggle with limited training data and poor generalization. In this study, we propose
Mengxuan Wan, Yanpeng Zhao, Yixin Zhang, Huiyan Xu, Duoyun Yi, Peng Zan, Song He, Xiaochen Bo   +7 more
doaj   +2 more sources

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion [PDF]

Scientific Reports
Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug
Youzhi Liu, Linlin Xing, Longbo Zhang, Hongzhen Cai, Maozu Guo   +4 more
doaj   +2 more sources

EMPDTA: An End-to-End Multimodal Representation Learning Framework with Pocket Online Detection for Drug–Target Affinity Prediction [PDF]

Molecules
Accurately predicting drug–target interactions is a critical yet challenging task in drug discovery. Traditionally, pocket detection and drug–target affinity prediction have been treated as separate aspects of drug–target interaction, with few methods ...
Dingkai Huang, Jiang Xie
doaj   +2 more sources

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction [PDF]

BMC Bioinformatics
Background The development of drug–target binding affinity (DTA) prediction tasks significantly drives the drug discovery process forward. Leveraging the rapid advancement of artificial intelligence, DTA prediction tasks have undergone a transformative ...
Jinghong Sun, Han Wang, Jia Mi, Jing Wan, Jingyang Gao   +4 more
doaj   +2 more sources

Drug-target affinity prediction using graph neural network and contact maps. [PDF]

RSC Adv, 2020
Prediction of drug–target affinity by constructing both molecule and protein graphs.
Jiang M, Li Z, Zhang S, Wang S, Wang X, Yuan Q, Wei Z.   +6 more
europepmc   +4 more sources

GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information [PDF]

Biomolecules
Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has
Kusal Debnath, Pratip Rana, Preetam Ghosh   +2 more
doaj   +2 more sources

A geometric graph-based deep learning model for drug-target affinity prediction [PDF]

BMC Bioinformatics
In structure-based drug design, accurately estimating the binding affinity between a candidate ligand and its protein receptor is a central challenge.
Md Masud Rana, Farjana Tasnim Mukta, Duc D. Nguyen   +2 more
doaj   +2 more sources

MDNN-DTA: a multimodal deep neural network for drug-target affinity prediction [PDF]

Frontiers in Genetics
Determining drug-target affinity (DTA) is a pivotal step in drug discovery, where in silico methods can significantly improve efficiency and reduce costs.
Xu Gao, Xu Gao, Mengfan Yan, Mengfan Yan, Chengwei Zhang, Chengwei Zhang, Gang Wu, Gang Wu, Jiandong Shang, Jiandong Shang, Congxiang Zhang, Congxiang Zhang, Kecheng Yang, Kecheng Yang   +13 more
doaj   +2 more sources

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

ACS Omega
Predicting the drug-target binding affinity (DTA) is crucial in drug discovery, and an increasing number of researchers are using artificial intelligence techniques to make such predictions. Many effective deep neural network prediction models have been proposed. However, current methods need improvement in accuracy, complexity, and efficiency. In this
Li Han, Ling Kang, Quan Guo
doaj   +3 more sources