Integrating Structural Graphs and Cross-Attention for Drug–Target Interaction Prediction
Bibliographic record
Abstract
Artificial intelligence (AI) has significantly improved drug discovery, particularly in drug-target interaction (DTI) prediction, by enabling faster and more accurate identification of therapeutic compounds. In this work, we propose a structure-aware deep learning framework that leverages graphbased representations for both drugs and proteins. Drugs are modeled using a Topology Adaptive Graph Convolutional Network (TAGCN), while proteins are represented as residue-level graphs processed through a dual Weighted Graph Convolutional Network (WGCN) incorporating distance and angle features. After multi-layer representation learning, a dual cross-attention module aligns relevant substructures between drugs and proteins to predict interactions more effectively. Evaluated on the updated Yamanishi benchmark dataset, our method outperforms several strong baselines, achieving an F1-score of 89% and a ROC-AUC of 95%. These results highlight the importance of integrating detailed structural information and cross-modal attention for advancing AI-driven DTI prediction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".