GATRsite: RNA–Ligand Binding Site Prediction Using Graph Attention Networks and Pretrained RNA Language Models
Bibliographic record
Abstract
Identifying functional sites of RNA, particularly those where small molecules bind, is crucial for understanding related biological processes and advancing drug design. Small molecule therapies, compared to traditional protein-targeted therapies, have the potential to pioneer novel RNA-specific therapeutic strategies. However, the challenge lies in developing accurate and efficient computational methods, requiring novel computational models that can better characterize RNA and precisely predict RNA-small molecule binding sites. In this study, we introduced GATRsite, an efficient deep learning framework leveraging graph attention networks (GATs) and Pretrained RNA Language Models to predict RNA-ligand binding sites. GATRsite regards RNA nucleotides as nodes, and its main component is an RNA graph with nodes that comprehensively incorporates both sequential and structural features. Furthermore, it integrates embeddings derived from advanced Pretrained RNA Language Models, which precisely capture the intricate structural and functional complexities of RNA molecules. GATRsite outperforms other state-of-the-art methods, particularly in terms of recall rates, Matthew's correlation coefficient, and F1 score on benchmark test sets. Moreover, GATRsite exhibits significant robustness regarding the predicted RNA structures. A user-friendly online server for GATRsite is freely available at https://malab.sjtu.edu.cn/GATRsite/.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".