Results 31 to 40 of about 345,726 (229)
SAG-DTA: Prediction of Drug-Target Affinity Using Self-Attention Graph Network. [PDF]
Int J Mol Sci, 2021 Zhang S +5 more
europepmc +2 more sources
DeepDTA: deep drug–target binding affinity prediction [PDF]
Bioinformatics, 2018 Abstract Motivation The identification of novel drug–target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT ...
Hakime Öztürk +2 more
openaire +3 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
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 +4 more
openaire +2 more sources
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 +3 more
openaire +1 more source
Hierarchical graph representation learning for the prediction of drug-target binding affinity
Information Sciences, 2022 The identification of drug-target binding affinity (DTA) has attracted increasing attention in the drug discovery process due to the more specific interpretation than binary interaction prediction. Recently, numerous deep learning-based computational methods have been proposed to predict the binding affinities between drugs and targets benefiting from ...
Zhaoyang Chu +6 more
openaire +3 more sources
WideDTA: prediction of drug-target binding affinity
CoRR, 2019 Motivation: Prediction of the interaction affinity between proteins and compounds is a major challenge in the drug discovery process. WideDTA is a deep-learning based prediction model that employs chemical and biological textual sequence information to predict binding affinity.
Hakime Öztürk +2 more
openaire +2 more sources
GraphDTA: Predicting drug–target binding affinity with graph neural networks [PDF]
Bioinformatics, 2019 Abstract The development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs.
Thin Nguyen +5 more
openaire +2 more sources
BMC Bioinformatics, 2021 Background The study of drug–target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure–activity relationship (QSAR) and molecular docking (MD) are most common methods in research ...
Xian-rui Wang +4 more
doaj +1 more source
PLoS Computational Biology, 2023 Drug-target binding affinity prediction plays a key role in the early stage of drug discovery. Numerous experimental and data-driven approaches have been developed for predicting drug-target binding affinity.
Mahmood Kalemati +2 more
doaj +1 more source