Fragment Libraries from Large and Novel Synthetic Compounds and Natural Products: A Comparative Chemoinformatic Analysis
Bibliographic record
Abstract
We report comprehensive fragment libraries obtained from large natural product databases and compare their chemical space coverage and diversity with those of synthetic fragment libraries. Specifically, we obtained 2,583,127 fragments derived from the recently updated collection of open natural product (COCONUT) data set with more than 695,133 unique (nonduplicate) natural products and 74,193 fragments derived from the Latin America Natural Product Database (LANaPDB) with 13,578 unique natural products from Latin America. The content, chemical space coverage, and chemical diversity of the natural product libraries were compared to the recently developed CRAFT library, which contains 1214 fragments based on distinct heterocyclic scaffolds and natural product-derived chemicals. The fragment libraries herein obtained and curated are freely available at https://github.com/DIFACQUIM/Fragment-libraries-from-large-synthetic-compounds-and-natural-products-collections.git.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".