African Natural Products Database (ANPDB): A resource for exploring the therapeutic potential of natural compounds from Africa
Bibliographic record
Abstract
The African Natural Products Database (ANPDB) is a comprehensive repository currently encompassing over 11 000 natural compounds sourced from diverse species across the African continent. ANPDB integrates both experimental and predicted NMR and MS data to enhance compound annotation and facilitate dereplication, utilizing machine learning algorithms for spectral simulation and fragment prediction. The database offers a range of methods for compound structure search, including substructure matching, chemical similarity analysis, and property-based filtering, empowering users to efficiently explore chemical diversity. ANPDB also compiles detailed metadata on the traditional medicinal uses of source organisms, linking ethnobotanical knowledge with chemical and biological data. By consolidating information on biological sources, bioactivities, traditional applications, and bibliographic references (currently ranging from 1961 to 2024), ANPDB provides a robust platform for drug discovery, natural products research, and the exploration of Africa's unique chemical and medicinal biodiversity. The database is open access, and the entire dataset and metadata are available for download and for computer-aided drug design endeavours. Link to the website: https://african-compounds.org.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.019 |
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".