A Multi-input Deep Learning Architecture for STAT3 Inhibitor Prediction
Bibliographic record
Abstract
Signal transducer and activator of transcription 3 (STAT3) is a critical factor involved in various physiological and oncogenic signaling pathways. Machine learning models are valuable tools for predicting or screening STAT3 inhibitors. However, the predictive performance and interpretability of existing models still require improvement. In this study, we introduce a fingerprint-enhanced graph (FPG) attention network model, which integrates sequence-based fingerprints and structure-based graph representations to predict STAT3 inhibitors. During the feature learning process, the FPG model converts sequence information into a fingerprint vector, while structural information is encoded into a separate vector using a graph attention network module. These two vectors are then concatenated and passed through a multilayer perceptron for molecular activity classification. Among 49 models with various representations and algorithm combinations, the FPG-based model achieved the best predictive performance, with an average area under the curve of 0.897 on the test set. Furthermore, the model outperformed existing prediction models for identifying STAT3 inhibitors. Additionally, fingerprint analysis and attention heatmaps, combined with SHAP algorithms, provided valuable insights into the structure-activity relationship of STAT3 inhibitors, enhancing model interpretability. To facilitate related research and applications, we developed a web service (STAT3 Pro: https://gzliang.cqu.edu.cn/software/Stat3Pro.html) for STAT3 inhibitor 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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".