DGDTA: dynamic graph attention network for predicting drug–target binding affinity [PDF]
BMC Bioinformatics, 2023 Background Obtaining accurate drug–target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be ...
Haixia Zhai +5 more
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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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Enhanced information cross-attention fusion for drug–target binding affinity prediction [PDF]
PeerJ Computer ScienceBackground The rapid development of artificial intelligence has permeated many fields, with its application in drug discovery becoming increasingly mature.
Ailu Fei +5 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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GANsDTA: Predicting Drug-Target Binding Affinity Using GANs [PDF]
Frontiers in Genetics, 2020 The computational prediction of interactions between drugs and targets is a standing challenge in drug discovery. State-of-the-art methods for drug-target interaction prediction are primarily based on supervised machine learning with known label ...
Lingling Zhao +4 more
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Prediction of drug–target binding affinity using similarity-based convolutional neural network [PDF]
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
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DrugForm-DTA: Towards real-world drug-target binding affinity model [PDF]
Computational and Structural Biotechnology JournalDrug-target affinity (DTA) prediction is a fundamental challenge in drug discovery. Computational methods for predicting DTA can greatly assist drug design by narrowing the search space and reducing the number of protein-ligand complexes with low ...
Ivan Khokhlov +8 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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Drug-target binding affinity prediction based on power graph and word2vec [PDF]
BMC Medical GenomicsBackground Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development.
Jing Hu +4 more
doaj +2 more sources