Results 11 to 20 of about 345,726 (229)

Sequence-based drug-target affinity prediction using weighted graph neural networks [PDF]

BMC Genomics, 2022
Background Affinity prediction between molecule and protein is an important step of virtual screening, which is usually called drug-target affinity (DTA) prediction. Its accuracy directly influences the progress of drug development.
Mingjian Jiang, Shuang Wang, Shugang Zhang, Wei Zhou, Yuanyuan Zhang, Zhen Li   +5 more
doaj   +2 more sources

A deep learning method for drug-target affinity prediction based on sequence interaction information mining [PDF]

PeerJ, 2023
Background A critical aspect of in silico drug discovery involves the prediction of drug-target affinity (DTA). Conducting wet lab experiments to determine affinity is both expensive and time-consuming, making it necessary to find alternative approaches.
Mingjian Jiang, Yunchang Shao, Yuanyuan Zhang, Wei Zhou, Shunpeng Pang   +4 more
doaj   +3 more sources

DCI-SiteDTA: drug-target affinity prediction based on binding sites detection and site-aware dual cross-interaction block [PDF]

BMC Bioinformatics
Background Predicting the binding affinity between drugs and proteins is crucial for accelerating drug discovery. However, traditional research methods typically treat binding site detection and affinity prediction as two separate tasks, lacking ...
Jinyang Zhang, Xingyang Li, Bo Wei, Yuni Zeng   +3 more
doaj   +2 more sources

Structure-free drug–target affinity prediction using protein and molecule language models [PDF]

Journal of Cheminformatics
Accurate prediction of drug-target affinity (DTA) is crucial for advancing drug discovery and optimizing experimental processes. Traditional DTA models often rely on handcrafted features or structural data, which can limit their generalizability and ...
Amir Hallaji Bidgoli, Morteza Mahdavi, Hamed Malek   +2 more
doaj   +2 more sources

Prediction of Drug-Target Affinity Using Attention Neural Network. [PDF]

Int J Mol Sci
Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational
Tang X, Lei X, Zhang Y.
europepmc   +3 more sources

FingerDTA: A Fingerprint-Embedding Framework for Drug-Target Binding Affinity Prediction

Big Data Mining and Analytics, 2023
Many efforts have been exerted toward screening potential drugs for targets, and conducting wet experiments remains a laborious and time-consuming approach.
Xuekai Zhu, Juan Liu, Jian Zhang, Zhihui Yang, Feng Yang, Xiaolei Zhang   +5 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

SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features [PDF]

BMC Bioinformatics, 2023
Background Drug–target affinity (DTA) prediction is a critical step in the field of drug discovery. In recent years, deep learning-based methods have emerged for DTA prediction.
Shourun Pan, Leiming Xia, Lei Xu, Zhen Li   +3 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