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Graph neural pre-training based drug-target affinity prediction.
- Source :
- Frontiers in Genetics; 2024, p1-12, 12p
- Publication Year :
- 2024
-
Abstract
- Computational drug-target affinity prediction has the potential to accelerate drug discovery. Currently, pre-training models have achieved significant success in various fields due to their ability to train the model using vast amounts of unlabeled data. However, given the scarcity of drug-target interaction data, pre-training models can only be trained separately on drug and target data, resulting in features that are insufficient for drug-target affinity prediction. To address this issue, in this paper, we design a graph neural pre-training-based drug-target affinity prediction method (GNPDTA). This approach comprises three stages. In the first stage, two pre-training models are utilized to extract low-level features from drug atom graphs and target residue graphs, leveraging a large number of unlabeled training samples. In the second stage, two 2D convolutional neural networks are employed to combine the extracted drug atom features and target residue features into high-level representations of drugs and targets. Finally, in the third stage, a predictor is used to predict the drug-target affinity. This approach fully utilizes both unlabeled and labeled training samples, enhancing the effectiveness of pre-training models for drug-target affinity prediction. In our experiments, GNPDTA outperforms other deep learning methods, validating the efficacy of our approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 16648021
- Database :
- Complementary Index
- Journal :
- Frontiers in Genetics
- Publication Type :
- Academic Journal
- Accession number :
- 179988648
- Full Text :
- https://doi.org/10.3389/fgene.2024.1452339