Dataset Information

3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs.

ABSTRACT: Accurate prediction of the drug-target affinity (DTA) in silico is of critical importance for modern drug discovery. Computational methods of DTA prediction, applied in the early stages of drug development, are able to speed it up and cut its cost significantly. A wide range of approaches based on machine learning were recently proposed for DTA assessment. The most promising of them are based on deep learning techniques and graph neural networks to encode molecular structures. The recent breakthrough in protein structure prediction made by AlphaFold made an unprecedented amount of proteins without experimentally defined structures accessible for computational DTA prediction. In this work, we propose a new deep learning DTA model 3DProtDTA, which utilises AlphaFold structure predictions in conjunction with the graph representation of proteins. The model is superior to its rivals on common benchmarking datasets and has potential for further improvement.

SUBMITTER: Voitsitskyi T

PROVIDER: S-EPMC10065141 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs.

Voitsitskyi Taras T Stratiichuk Roman R Koleiev Ihor I Popryho Leonid L Ostrovsky Zakhar Z Henitsoi Pavlo P Khropachov Ivan I Vozniak Volodymyr V Zhytar Roman R Nechepurenko Diana D Yesylevskyy Semen S Nafiiev Alan A Starosyla Serhii S

RSC advances 20230331 15

Accurate prediction of the drug-target affinity (DTA) <i>in silico</i> is of critical importance for modern drug discovery. Computational methods of DTA prediction, applied in the early stages of drug development, are able to speed it up and cut its cost significantly. A wide range of approaches based on machine learning were recently proposed for DTA assessment. The most promising of them are based on deep learning techniques and graph neural networks to encode molecular structures. The recent ...[more]

PMID: 37006369

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Project description:Computational drug-target affinity prediction is important for drug screening and discovery. Currently, self-supervised learning methods face two major challenges in drug-target affinity prediction. The first difficulty lies in the phenomenon of sample mismatch: self-supervised learning processes drug and target samples independently, while actual prediction requires the integration of drug-target pairs. Another challenge is the mismatch between the broadness of self-supervised learning objectives and the precision of biological mechanisms of drug-target affinity (i.e., the induced-fit principle). The former focuses on global feature extraction, while the latter emphasizes the importance of local precise matching. To address these issues, an adaptive self-supervised learning-based drug-target affinity prediction (ASSLDTA) was designed. ASSLDTA integrates a novel adaptive self-supervised learning (ASSL) module with a high-level feature learning network to extract the feature. The ASSL leverages a large amount of unlabeled training data to effectively capture low-level features of drugs and targets. Its goal is to maximize the retention of original feature information, thereby bridging the objective gap between self-supervised learning and drug-target affinity prediction and alleviating the sample mismatch problem. The high-level feature learning network, on the other hand, focuses on extracting effective high-level features for affinity prediction through a small amount of labeled data. Through this two-stage feature extraction design, each stage undertakes specific tasks, fully leveraging the advantages of each model while efficiently integrating information from different data sources, providing a more accurate and comprehensive solution for drug-target affinity prediction. In our experiments, ASSLDTA is much better than other deep methods, and the result of ASSLDTA is significantly increased by learning adaptive self-supervised learning-based features, which validates the effectiveness of our ASSLDTA.

Dataset Information

3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs.

Publications

3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs.

Similar Datasets

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