Results 31 to 40 of about 71,218 (232)
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
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
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
Graph–sequence attention and transformer for predicting drug–target affinity
RSC Advances, 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.
Xiangfeng Yan, Yong Liu
openaire +2 more sources
Biomedicines, 2022 Drug-target binding affinity (DTA) prediction is an essential step in drug discovery. Drug-target protein binding occurs at specific regions between the protein and drug, rather than the entire protein and drug.
Haelee Bae, Hojung Nam
doaj +1 more source
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
Prediction of drug–target binding affinity using similarity-based convolutional neural network
Scientific Reports, 2021 Identifying novel drug–target interactions (DTIs) plays an important role in drug discovery. Most of the computational methods developed for predicting DTIs use binary classification, whose goal is to determine whether or not a drug–target (DT) pair ...
Jooyong Shim +3 more
doaj +1 more source
Scientific Reports, 2022 Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual drug screening. Most DTI prediction methods cast the problem as a binary classification task to predict if interactions exist or as a regression task to ...
Maha A. Thafar +5 more
doaj +1 more source
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. Consequently, developing computational approaches capable of identifying potential DTIs with minimum error rate are increasingly being pursued.
Maha Thafar +6 more
openaire +4 more sources