Explainable deep drug–target representations for binding affinity prediction
BMC Bioinformatics, 2022 Background Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug–target interactions and new leads. However, most of these methodologies have been overlooking the importance of providing
Nelson R. C. Monteiro +5 more
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Predicting drug-target binding affinity with cross-scale graph contrastive learning. [PDF]
Brief Bioinform, 2023 Abstract Identifying the binding affinity between a drug and its target is essential in drug discovery and repurposing. Numerous computational approaches have been proposed for understanding these interactions. However, most existing methods only utilize either the molecular structure information of drugs and targets or the interaction ...
Wang J, Xiao Y, Shang X, Peng J.
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Deep drug-target binding affinity prediction with multiple attention blocks. [PDF]
Brief Bioinform, 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.
Zeng Y, Chen X, Luo Y, Li X, Peng D.
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DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network [PDF]
Current Issues in Molecular Biology, 2022 Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and ...
Lei Deng +4 more
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BiComp-DTA: Drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach. [PDF]
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
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Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning [PDF]
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
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GraphATT-DTA: Attention-Based Novel Representation of Interaction to Predict Drug-Target Binding Affinity [PDF]
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
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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 ...
Öztürk H, Özgür A, Ozkirimli E.
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FingerDTA: A Fingerprint-Embedding Framework for Drug-Target Binding Affinity Prediction
Big Data Mining and Analytics, 2023 Many efforts have been exerted toward screening potential drugs for targets, and conducting wet experiments remains a laborious and time-consuming approach.
Xuekai Zhu +5 more
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SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity [PDF]
BMC BiologyBackground Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research.
Xun Wang +6 more
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