ArticleQuantitative biology (Beijing, China)2026
BiGraph-DTA: Predicting drug-target interactions of hepatoprotective agents with graph convolutional networks.
Article in Quantitative biology (Beijing, China), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- BiGraph-DTA: Predicting drug-target interactions of hepatoprotective agents with graph convolutional networks.Quantitative biology (Beijing, China) · 2026Article
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5 authors.
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Abstract
Predicting drug-target affinity (DTA) is critical for discovering and developing hepatoprotective agents that can prevent and treat liver diseases. In this study, we propose BiGraph-DTA, a new predictive model for identifying DTA score prediction for hepatoprotective compounds by combining graph convolutional networks and bidirectional long short-term memory networks. This model is based on powerful frameworks that process both graph representations of molecular structures and sequential information from protein sequences to capture complex dependencies and interactions. Leveraging a curated hepatoprotective dataset (from ChEMBL) consisting of 21,421 interactions, the model outperforms traditional machine learning methods (such as random forest and XGBoost) as well as other deep learning methods (such as DeepDTA and GraphDTA) in terms of predictive performance. The BiGraph-DTA obtained the best mean squared error of 0.7885,
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