Results 11 to 20 of about 307,869 (200)

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

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

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

A dual-branch graph neural network architecture for drug-target binding affinity prediction [PDF]

Scientific Reports
Graph Neural Networks have emerged as a powerful paradigm for artificial intelligence driven drug discovery, offering molecular representation learning that surpasses many conventional approaches.
Khushnood Abbas, Chen Hao, Shi Dong, Wengang Zhou, Yong Tang, Shayla Islam, Mohammad Kamrul Hasan, Siti Norul Huda Sheikh Abdullah, Ruizhe Hou   +8 more
doaj   +2 more sources

PRGNet: a Parallel Residual Graph Network for enhanced drug-target binding affinity prediction [PDF]

BMC Genomics
Predicting drug-target binding affinity (DTA) remains a cornerstone of structure-based drug discovery but is still constrained by fundamental methodological trade-offs.
Jing Liu, Aamir Mehmood, Haiqi Liu, Yishu Liu, Dongqing Wei, Daixi Li   +5 more
doaj   +2 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

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

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