BiGraph-DTA: Predicting drug-target interactions of hepatoprotective agents with graph convolutional networks

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, R ² of 0.7208, and concordance index of 0.8508. Our proposed architecture holds potential for accelerating the drug discovery process of hepatoprotective therapy by highlighting the framework through which candidate drugs and their corresponding protein targets can be identified based on robust data-driven knowledge. This model, therefore, provides a new opportunity for discovering new hepatoprotective compounds, which may also make it possible to speed up finding new liver disease drugs.