CSatDTA: Prediction of Drug-Target Binding Affinity Using Convolution Model with Self-Attention. [PDF]
Int J Mol Sci, 2022 Ghimire A, Tayara H, Xuan Z, Chong KT.
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MGraphDTA: deep multiscale graph neural network for explainable drug-target binding affinity prediction. [PDF]
Chem Sci, 2022 Predicting drug–target affinity (DTA) is beneficial for accelerating drug discovery. Graph neural networks (GNNs) have been widely used in DTA prediction.
Yang Z +3 more
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DataDTA: a multi-feature and dual-interaction aggregation framework for drug-target binding affinity prediction. [PDF]
Bioinformatics, 2023 Motivation Accurate prediction of drug–target binding affinity (DTA) is crucial for drug discovery. The increase in the publication of large-scale DTA datasets enables the development of various computational methods for DTA prediction.
Zhu Y, Zhao L, Wen N, Wang J, Wang C.
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MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction. [PDF]
J Chem Inf ModelDrug-target affinity (DTA) prediction is an important task in the early stages of drug discovery. Traditional biological approaches are time-consuming, effort-consuming, and resource-consuming due to the large size of genomic and chemical spaces ...
Ranjan A +3 more
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Drug-target binding affinity prediction using message passing neural network and self supervised learning. [PDF]
BMC Genomics, 2023 Background Drug-target binding affinity (DTA) prediction is important for the rapid development of drug discovery. Compared to traditional methods, deep learning methods provide a new way for DTA prediction to achieve good performance without much ...
Xia L +5 more
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NHGNN-DTA: a node-adaptive hybrid graph neural network for interpretable drug-target binding affinity prediction. [PDF]
Bioinformatics, 2023 Motivation Large-scale prediction of drug–target affinity (DTA) plays an important role in drug discovery. In recent years, machine learning algorithms have made great progress in DTA prediction by utilizing sequence or structural information of both ...
He H, Chen G, Chen CY.
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GraphCL-DTA: A Graph Contrastive Learning With Molecular Semantics for Drug-Target Binding Affinity Prediction [PDF]
IEEE journal of biomedical and health informatics, 2023 Drug-target binding affinity prediction plays an important role in the early stages of drug discovery, which can infer the strength of interactions between new drugs and new targets. However, the performance of previous computational models is limited by
Xinxing Yang, Gen-ke Yang, Jian Chu
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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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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
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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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