Results 11 to 20 of about 522,643 (238)

GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity [PDF]

BMC Genomics
Background Drug-target binding affinity (DTA) prediction is vital in drug discovery and repositioning, more and more researchers are beginning to focus on this. Many effective methods have been proposed.
Junwei Luo, Ziguang Zhu, Zhenhan Xu, Chuanle Xiao, Jingjing Wei, Jiquan Shen   +5 more
doaj   +2 more sources

SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity [PDF]

BMC Biology
Background Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research.
Xun Wang, Zhijun Xia, Runqiu Feng, Tongyu Han, Hanyu Wang, Wenqian Yu, Xingguang Wang   +6 more
doaj   +2 more sources

InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks [PDF]

Heliyon
Predicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance.
Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi   +2 more
doaj   +2 more sources

Drug-target binding affinity prediction using message passing neural network and self supervised learning [PDF]

BMC Genomics, 2023
Background Drug-target binding affinity (DTA) prediction is important for the rapid development of drug discovery. Compared to traditional methods, deep learning methods provide a new way for DTA prediction to achieve good performance without much ...
Leiming Xia, Lei Xu, Shourun Pan, Dongjiang Niu, Beiyi Zhang, Zhen Li   +5 more
doaj   +2 more sources

DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks [PDF]

BMC Genomics
Background In recent years, there has been a growing interest in utilizing computational approaches to predict drug-target binding affinity, aiming to expedite the early drug discovery process.
Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi   +2 more
doaj   +2 more sources

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

ACS Omega
Li Han, Ling Kang, Quan Guo
doaj   +2 more sources

Deep Drug–Target Binding Affinity Prediction Base on Multiple Feature Extraction and Fusion [PDF]

ACS Omega
Zepeng Li, Yuni Zeng, Mingfeng Jiang, Bo Wei   +3 more
doaj   +2 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   +1 more source

Explainable deep drug–target representations for binding affinity prediction

BMC Bioinformatics, 2022
Background Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug–target interactions and new leads. However, most of these methodologies have been overlooking the importance of providing
Nelson R. C. Monteiro, Carlos J. V. Simões, Henrique V. Ávila, Maryam Abbasi, José L. Oliveira, Joel P. Arrais   +5 more
doaj   +1 more source

Evolutionary conservation of influenza A PB2 sequences reveals potential target sites for small molecule inhibitors. [PDF]

, 2017
The influenza A basic polymerase protein 2 (PB2) functions as part of a heterotrimer to replicate the viral RNA genome. To investigate novel PB2 antiviral target sites, this work identified evolutionary conserved regions across the PB2 protein sequence ...
Kukol, A., Kukol, A., Patel, H., Patel, H.   +3 more
core   +3 more sources