Additional file 1 of ABT-MPNN: an atom-bond transformer-based message-passing neural network for molecular property prediction
Bibliographic record
Abstract
Additional file 1: Table S1. Atom and bond features. Table S2. 200 Molecular descriptors generated by RDKit. Table S3. Algorithm of Bond Attention. Table S4. Algorithm of Atom Attention. Fig. S1. Comparison of ablation experiments using 5-fold cross-validation (A) Performance evaluation of each fold for the classification task (ClinTox) measured with AUROC. Experiments settings: #1: baseline; #2: use bond attention (Transformer); #3: use bond attention (Fastformer); #4 use atom attention; #5 use atom attention with inter-atomic matrices #6 use bond attention (Fastformer) and atom attention; #7 use bond attention (Fastformer) and atom attention with inter-atomic matrices (B) Performance evaluation of each fold for the regression task (ESOL) measured by RMSE. The settings of each experiment in the regression task are identical to those in the classification one.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.715 | 0.237 |
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".