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Record W4412761381 · doi:10.1021/acs.jnatprod.5c00446

Value Addition to African Natural Product-Based Drug Discovery Initiatives

2025· review· en· W4412761381 on OpenAlex
Godfrey Mayoka, Peter Mubanga Cheuka, Phanankosi Moyo, Godwin Akpeko Dziwornu, Denzil R. Beukes

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Natural Products · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsDiscovery Centre
Fundersnot available
KeywordsNatural productDrug discoveryValue (mathematics)Product (mathematics)Traditional medicineChemistryMedicineComputer scienceBiologyStereochemistryMathematicsBioinformatics

Abstract

fetched live from OpenAlex

Natural products are vital to drug discovery, yet Africa's vast biodiversity remains underutilized. This perspective examines barriers limiting Africa's impact─such as weak infrastructure, limited translational capacity, and minimal integration of medicinal chemistry. We advocate for advancing beyond basic extraction to include systematic isolation, pharmacokinetics studies, and semisynthetic derivatization. Emphasis is placed on integrating AI, cheminformatics, and biotransformation, alongside embedding drug discovery training into academic curricula. Strengthening regional networks, fostering interdisciplinary collaborations, and securing Africa-sensitive funding are essential. Strategic implementation of these actions will enable Africa to harness its natural resources for global drug discovery and address local health challenges.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.868
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.327
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it