Molecular Property Prediction based on Bimodal Supervised Contrastive Learning
Bibliographic record
Abstract
The simplified molecular-input line-entry system (SMILES) and the molecular graph are commonly used in chem-informatics to represent a molecule. Transformers are widely used for encoding SMILES to learn the relationship between elements that are far away from each other, while Graph Convolutional Networks (GCNs) are popular in graph representation learning and mostly focus on local structures. Since different information can be extracted from the SMILES string and the molecular graph, their integration might benefit the molecular property prediction task. In this work, we propose a bimodal supervised contrastive learning (BSCL) framework to integrate the SMILES string and the molecular graph in a unified network. Furthermore, the vanilla supervised contrastive loss (SCL) is not suitable for regression tasks, hence we design a weighted SCL to solve the problem. Six publicly available molecular property datasets are used to evaluate the proposed BSCL method, and our results show that the proposed bimodal method is superior to using the SMILES string or the molecular graph alone. Our code is released at https://github.com syanl992/BSCL.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".