Results 11 to 20 of about 333,304 (136)

GEFA: Early Fusion Approach in Drug-Target Affinity Prediction [PDF]

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022
Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA) problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues.
Tri Minh Nguyen, Thin Nguyen, Thao Minh Le, Truyen Tran   +3 more
openaire   +5 more sources

CASTER-DTA: equivariant graph neural networks for predicting drug-target affinity. [PDF]

Brief Bioinform
Abstract Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, predicted protein 3D structures are readily available; however, scalable methods for predicting binding affinity
Kumar R, Romano JD, Ritchie MD.
europepmc   +4 more sources

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

Bioinformatics, 2023
AbstractMotivationNumerous 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. Feature extraction and optimization constitute a critical step that significantly influences the enhancement of model ...
Ru X, Zou Q, Lin C.
europepmc   +3 more sources

Structure-free drug–target affinity prediction using protein and molecule language models [PDF]

Journal of Cheminformatics
Accurate prediction of drug-target affinity (DTA) is crucial for advancing drug discovery and optimizing experimental processes. Traditional DTA models often rely on handcrafted features or structural data, which can limit their generalizability and ...
Amir Hallaji Bidgoli, Morteza Mahdavi, Hamed Malek   +2 more
doaj   +2 more sources

SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features [PDF]

BMC Bioinformatics, 2023
Background Drug–target affinity (DTA) prediction is a critical step in the field of drug discovery. In recent years, deep learning-based methods have emerged for DTA prediction.
Shourun Pan, Leiming Xia, Lei Xu, Zhen Li   +3 more
doaj   +2 more sources

EMPDTA: An End-to-End Multimodal Representation Learning Framework with Pocket Online Detection for Drug–Target Affinity Prediction [PDF]

Molecules
Accurately predicting drug–target interactions is a critical yet challenging task in drug discovery. Traditionally, pocket detection and drug–target affinity prediction have been treated as separate aspects of drug–target interaction, with few methods ...
Dingkai Huang, Jiang Xie
doaj   +2 more sources

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction [PDF]

BMC Bioinformatics
Background The development of drug–target binding affinity (DTA) prediction tasks significantly drives the drug discovery process forward. Leveraging the rapid advancement of artificial intelligence, DTA prediction tasks have undergone a transformative ...
Jinghong Sun, Han Wang, Jia Mi, Jing Wan, Jingyang Gao   +4 more
doaj   +2 more sources

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion [PDF]

Scientific Reports
Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug
Youzhi Liu, Linlin Xing, Longbo Zhang, Hongzhen Cai, Maozu Guo   +4 more
doaj   +2 more sources

DrugForm-DTA: Towards real-world drug-target binding affinity model

Computational and Structural Biotechnology Journal
Drug-target affinity (DTA) prediction is a fundamental challenge in drug discovery. Computational methods for predicting DTA can greatly assist drug design by narrowing the search space and reducing the number of protein-ligand complexes with low ...
Ivan Khokhlov, Anna Tashchilova, Nikolai Bugaev-Makarovskiy, Olga Glushkova, Vladimir Yudin, Anton Keskinov, Sergey Yudin, Dmitry Svetlichnyy, Veronika Skvortsova   +8 more
doaj   +3 more sources

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

RSC Adv, 2020
Prediction of drug–target affinity by constructing both molecule and protein graphs.
Jiang M, Li Z, Zhang S, Wang S, Wang X, Yuan Q, Wei Z.   +6 more
europepmc   +4 more sources