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
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ImageDTA: A Simple Model for Drug–Target Binding Affinity Prediction
ACS OmegaPredicting 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
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Drug-target affinity prediction using applicability domain based on data density [PDF]
, 2021 In the pursuit of research and development of drug discovery, the computational prediction of the target affinity of a drug candidate is useful for screening compounds at an early stage and for verifying the binding potential to an unknown target.
Shunya, Sugita, Masahito, Ohue
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Correction to: Breaking the barriers of data scarcity in drug-target affinity prediction. [PDF]
Brief Bioinform, 2023 europepmc +3 more sources
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
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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
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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
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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
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A Multibranch Neural Network for Drug-Target Affinity Prediction Using Similarity Information [PDF]
ACS OmegaJing Chen, Xiaolin Yang, Haoyu Wu
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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
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