BiGraph‐DTA: Predicting drug–target interactions of hepatoprotective agents with graph convolutional networks
Bibliographic record
Abstract
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, 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".