The Dynamic Connection Layer (DCL): Enhancing Topological Representation in Chemical Graph Neural Networks
Bibliographic record
Abstract
In graphical-based chemistry machine learning (ML) models, a precise atomic correlation depiction, known as molecular topology, can significantly increase the model's performance. However, owing to the high acquisition cost, existing chemical graph data often use chemical bonds or atomic distances as molecular topology proxies. Such approximation, though practical, inevitably introduces inaccurate information that amplifies the learning difficulty of models and ultimately detracts from their performance. Despite various available data preprocessing techniques that can mitigate potential damage from inaccurate topology, those complex and data-type-tailored methods limit their generalizability in dealing with different types of chemical graph data. This paper introduces the dynamic connection layer (DCL) to address this challenge in a more general framework. This innovative graph neural layer dynamically modifies the input "topological information" (whether represented by chemical bonds or distances) to obtain a more accurate molecular topology description while preserving the generality as a trainable neural layer. We assessed the efficacy of the DCL layer using a data set derived from QM9 and demonstrated its efficiency in processing chemical graph data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".