DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation [PDF]
Nature CommunicationsIdentifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug discovery
Pir Masoom Shah +5 more
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Drug-target binding affinity prediction based on power graph and word2vec
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
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MEGDTA: multi-modal drug-target affinity prediction based on protein three-dimensional structure and ensemble graph neural network [PDF]
BMC GenomicsBackground Drug development is a time-consuming and costly endeavor, and utilizing computer-aided methods to predict drug-target affinity (DTA) can significantly accelerate this process.
Zhanwei Hou +5 more
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Learnable protein representations in computational biology for predicting drug-target affinity
Journal of CheminformaticsIn this review, we discuss the various different types of learnable protein representations that have been used in computational biology, with a particular focus on representations that have been used in the paradigm of predicting drug-target affinity ...
Rachit Kumar +2 more
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Correction to: Breaking the barriers of data scarcity in drug-target affinity prediction. [PDF]
Brief Bioinform, 2023 europepmc +2 more sources
PRGNet: a Parallel Residual Graph Network for enhanced drug-target binding affinity prediction [PDF]
BMC GenomicsPredicting drug-target binding affinity (DTA) remains a cornerstone of structure-based drug discovery but is still constrained by fundamental methodological trade-offs.
Jing Liu +5 more
doaj +2 more sources
GEFA: Early Fusion Approach in Drug-Target Affinity Prediction [PDF]
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022 Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA) problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues.
Tri Minh Nguyen 0005 +3 more
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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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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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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
openaire +2 more sources