3D2SMILES: Translating Physical Molecular Models into Digital DeepSMILES Notations Using Deep Learning
Bibliographic record
Abstract
Physical molecular models are widely used in educational settings for teaching organic and other branches of chemistry, offering an intuitive understanding of molecular structures. Conversely, while less intuitive, virtual models provide additional functionalities, such as retrieving molecular names and other properties. Currently, to the best of our knowledge, there is a gap between 3D molecular models and their digital counterparts. This paper introduces a computer vision model designed to bridge this gap by converting images of physical molecular models into their digital DeepSMILES representations. This conversion facilitates further information retrieval, enhancing educational utility. We developed synthetic and real datasets to train our model and evaluated its performance across various dataset combinations. Additionally, we attempted to improve the model’s accuracy by multi-image input and beam search. We achieved 62.0% top1 accuracy and 80.3% top-3 accuracy with beam search and multi-image input on our validation set. We also explored the model’s characteristics, such as explainability by saliency maps, and examined its calibration. We also discussed the model’s limitations and directions for future research.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".