FAIR Cheminformatics - A concept to enhance interoperability and reuseability of chemical structures and reactions
Bibliographic record
Abstract
A key challenge in cheminformatics, in particular with respect to chemical exchange, is ambiguity in chemical structure interpretation. For example, depending on factors such as the chemical structure format, the structure representation, and the software and systems involved, a compound with a single stereocenter and a wedge bond might be interpreted as the respective enantiomer, or as a racemic mixture. Many of the pitfalls can be avoided or circumnavigated if one stays within the subset of cheminformatics (formats, representations, etc.) which does not have such ambiguity. It is proposed to explicitly define this subset and promote it as a "Standard for FAIR Cheminformatics". A concept for such a Standard for FAIR Cheminformatics is outlined. This approach is envisaged to bring major improvements to the overall FAIRness (Findability, Accessibility, Interoperability, Reuse) of chemical structures and reactions, in particular with respect to interoperability and reuse, within reasonable effort and in a comparatively short time frame.
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 | 0.091 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.020 | 0.049 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".