Results 31 to 40 of about 141,734 (283)

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

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

Learnable protein representations in computational biology for predicting drug-target affinity

Journal of Cheminformatics
In this review, we discuss the various different types of learnable protein representations that have been used in computational biology, with a particular focus on representations that have been used in the paradigm of predicting drug-target affinity ...
Rachit Kumar, Joseph Romano, Marylyn Ritchie   +2 more
doaj   +3 more sources

GraphPrint: Extracting Features from 3D Protein Structure for Drug Target Affinity Prediction

arXiv.org
Accurate drug target affinity prediction can improve drug candidate selection, accelerate the drug discovery process, and reduce drug production costs. Previous work focused on traditional fingerprints or used features extracted based on the amino acid ...
Amritpal Singh
semanticscholar   +3 more sources

Drug-target affinity prediction using graph neural network and contact maps. [PDF]

RSC Adv, 2020
Computer-aided drug design uses high-performance computers to simulate the tasks in drug design, which is a promising research area. Drug–target affinity (DTA) prediction is the most important step of computer-aided drug design, which could speed up drug
Jiang M, Li Z, Zhang S, Wang S, Wang X, Yuan Q, Wei Z.   +6 more
europepmc   +2 more sources

Impact of Protein Representations on Drug-Target Affinity Prediction

and target proteins can significantly hasten the drug discovery and development process. Utilizing artificial intelligence (AI) models to predict drug-target affinity (DTA) is an affordable and efficient strategy for sifting out undesirable molecules and identifying promising drug candidates.
Marijan, Matija, Tanasijević, Ivan
openaire   +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

Dual modality feature fused neural network integrating binding site information for drug target affinity prediction. [PDF]

NPJ Digit Med
Accurately predicting binding affinities between drugs and targets is crucial for drug discovery but remains challenging due to the complexity of modeling interactions between small drug and large targets.
He H, Chen G, Tang Z, Chen CY.
europepmc   +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

Optimization of drug-target affinity prediction methods through feature processing schemes. [PDF]

Bioinformatics, 2023
Motivation Numerous high-accuracy drug–target affinity (DTA) prediction models, whose performance is heavily reliant on the drug and target feature information, are developed at the expense of complexity and interpretability.
Ru X, Zou Q, Lin C.
europepmc   +2 more sources

Correction to: Breaking the barriers of data scarcity in drug-target affinity prediction. [PDF]

Brief Bioinform, 2023
europepmc   +2 more sources