Results 21 to 30 of about 342,422 (269)

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

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

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

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

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

MGF-DTA: A Multi-Granularity Fusion Model for Drug-Target Binding Affinity Prediction. [PDF]

Int J Mol Sci
Drug–target affinity (DTA) prediction is one of the core components of drug discovery. Despite considerable advances in previous research, DTA tasks still face several limitations with insufficient multi-modal information of drugs, the inherent sequence length limitation of protein language models, and single attention mechanisms that fail to capture ...
Ni Z, Wei B, Zeng Y.
europepmc   +4 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

MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction. [PDF]

J Chem Inf Model
Drug-target affinity (DTA) prediction is an important task in the early stages of drug discovery. Traditional biological approaches are time-consuming, effort-consuming, and resource-consuming due to the large size of genomic and chemical spaces. Computational approaches using machine learning have emerged to narrow down the drug candidate search space.
Ranjan A, Bess A, Alvin C, Mukhopadhyay S.   +3 more
europepmc   +3 more sources