Results 1 to 10 of about 5,903,465 (247)

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

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

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities [PDF]

Frontiers in Chemistry, 2019
The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery.
Maha Thafar, Maha Thafar, Arwa Bin Raies, Somayah Albaradei, Somayah Albaradei, Magbubah Essack, Vladimir B. Bajic   +6 more
doaj   +4 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

CSatDTA: Prediction of Drug-Target Binding Affinity Using Convolution Model with Self-Attention. [PDF]

Int J Mol Sci, 2022
Ghimire A, Tayara H, Xuan Z, Chong KT.
europepmc   +2 more sources

MSGNN-DTA: Multi-Scale Topological Feature Fusion Based on Graph Neural Networks for Drug-Target Binding Affinity Prediction. [PDF]

Int J Mol Sci, 2023
Wang S, Song X, Zhang Y, Zhang K, Liu Y, Ren C, Pang S.   +6 more
europepmc   +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

Multilevel Attention Models for Drug Target Binding Affinity Prediction [PDF]

Neural Processing Letters, 2021
Drug-Target Binding Affinity (DTBA) prediction is one class of Drug-Target Interaction problem (DTI), where the focus is to predict the binding strength of a drug-target pair. Several machine learning approaches have been developed for this purpose. However, almost all rely on the use of increasingly sophisticated inputs to improve the obtained results
openaire   +1 more source

Drug-Target Binding Affinity Prediction Using Transformers [PDF]

, 2021
Abstract Drug discovery is generally difficult, expensive, and low success rate. One of the essential steps in the early stages of drug discovery and drug repurposing is identifying drug-target interactions. Binding affinity indicates the strength of drug-target pair interactions.
Mahsa Saadat, Armin Behjati, Fatemeh Zare-Mirakabad, Sajjad Gharaghani   +3 more
openaire   +1 more source