Molecular Structure Learning with Graph Transformers: A Graph Reduction Approach for Improved Efficiency and Accuracy
Bibliographic record
Abstract
Transformer models have demonstrated impressive performance across various domains, yet their application to non-NLP fields, such as chemical and biological informatics, remains challenging due to difficulties in tokenizing and embedding complex data structures. While combining Large Language Models (LLMs) with large-scale pretraining shows potential, issues like computational cost, memory usage, and overfitting still pose significant obstacles.In this work, we present modifications to the Graphormer architecture aimed at addressing these limitations. Our contributions include a chemically intuitive graph reduction strategy, structural feature normalization, and a learnable graph readout. These improvements enhance efficiency and prediction accuracy while significantly reducing training time. Notably, our hierarchical pooling approach compresses graphs by reducing node count while preserving essential structural features, enabling more effective learning. Validation on molecular datasets demonstrates that our method reduces training time by an average of 98.35% and decreases testing error by approximately 47.97%. These results highlight the potential of our approach for large-scale pretraining and downstream molecular property prediction. Our work emphasizes the value of domain-aware architectural adaptations for applying graph transformers in chemical and biological contexts.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".