Additional file 1 of ABT-MPNN: an atom-bond transformer-based message-passing neural network for molecular property prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.967 | 0.000 |
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 it