Results 21 to 30 of about 600,341 (272)

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

BMC Bioinformatics, 2023
Background Obtaining accurate drug–target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be ...
Haixia Zhai, Hongli Hou, Junwei Luo, Xiaoyan Liu, Zhengjiang Wu, Junfeng Wang   +5 more
doaj   +3 more sources

DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation [PDF]

Nature Communications
Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug discovery
Pir Masoom Shah, Huimin Zhu, Zhangli Lu, Kaili Wang, Jing Tang, Min Li   +5 more
doaj   +2 more sources

Drug-target binding affinity prediction based on power graph and word2vec

BMC Medical Genomics
Background Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development.
Jing Hu, Shuo Hu, Minghao Xia, Kangxing Zheng, Xiaolong Zhang   +4 more
doaj   +3 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

Graph-sequence attention and transformer for predicting drug-target affinity. [PDF]

RSC Adv, 2022
We proposed a novel model based on self-attention, called GSATDTA, to predict the binding affinity between drugs and targets. Experimental results show that our model outperforms the state-of-the-art methods on two independent datasets.
Yan X, Liu Y.
europepmc   +3 more sources

Drug-Target Binding Affinity Prediction Using Transformers [PDF]

, 2021
AbstractDrug 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

Deep drug-target binding affinity prediction with multiple attention blocks [PDF]

Briefings in Bioinformatics, 2021
Abstract Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs.
Yuni, Zeng, Xiangru, Chen, Yujie, Luo, Xuedong, Li, Dezhong, Peng   +4 more
openaire   +2 more sources

Prediction of Drug-Target Affinity Using Attention Neural Network. [PDF]

Int J Mol Sci
Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational
Tang X, Lei X, Zhang Y.
europepmc   +3 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