KGPrompt-DTA: Knowledge Graph Prompt-Enhanced Graph-Transformer for Drug-Target Binding Affinity Prediction
Bibliographic record
Abstract
Accurately predicting drug-target binding affinity (DTA) is essential for accelerating drug discovery and reducing experimental costs. Existing deep learning-based DTA methods often fail to effectively integrate heterogeneous biological knowledge and structural information, leading to suboptimal generalization across diverse drug-target pairs. In this work, we propose KGPrompt-DTA, a novel framework that injects knowledge graph-driven prompts into a hybrid graph-transformer architecture to enhance molecular and protein representation learning. Specifically, we construct entity-relation-aware prompts from biomedical knowledge graphs and dynamically fuse them with graph-encoded molecular and protein features via a multilevel attention mechanism. This design enables the model to capture both local structural dependencies and high-level semantic associations. Extensive experiments on two benchmark datasets, DAVIS and KIBA, demonstrate that KGPrompt-DTA consistently outperforms state-of-the-art methods, achieving MSE reductions of 15.5 % and 6.5 %, respectively, while also improving CI,$r_{m}^{2}$, and Pearson correlation scores. Ablation studies confirm the contribution of each component, highlighting the effectiveness of integrating knowledge-informed prompts with graph-transformer learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".