Results 11 to 20 of about 159,394 (268)

GEFA: Early Fusion Approach in Drug-Target Affinity Prediction [PDF]

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022
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.
Tri Minh Nguyen, Thin Nguyen, Thao Minh Le, Truyen Tran   +3 more
openaire   +5 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

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

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

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

DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation [PDF]

Nature Communications
Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug discovery
Pir Masoom Shah, Huimin Zhu, Zhangli Lu, Kaili Wang, Jing Tang, Min Li   +5 more
doaj   +2 more sources