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Record W4404707458 · doi:10.1093/nar/gkae1093

The Natural Products Atlas 3.0: extending the database of microbially derived natural products

2024· article· en· W4404707458 on OpenAlexafffund
Ella F Poynton, Jeffrey A. van Santen, Matthew Pin, Emily McMann, Jonathan Parra, Brandon Showalter, Liana Zaroubi, Katherine Duncan, Roger G. Linington

Bibliographic record

VenueNucleic Acids Research · 2024
Typearticle
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsSimon Fraser University
FundersH2020 Marie Skłodowska-Curie ActionsNatural Sciences and Engineering Research Council of CanadaDepartment for Environment, Food and Rural Affairs, UK GovernmentUK Research and InnovationHORIZON EUROPE Framework ProgrammeGovernment of the United Kingdom
KeywordsDatabaseAtlas (anatomy)Natural productSuiteComputer scienceBiologyInformation retrieval

Abstract

fetched live from OpenAlex

The Natural Products Atlas is a database of microbially derived natural products that contains structures, producing organism taxonomy, biosynthetic and chemical ontology classifications, grouping by compound classes and cross-links to a suite of other natural product-related data resources. The database is supported by a web server that includes functionality to browse the collection, search the database using both chemical structures and text/numerical terms and visualize the chemical diversity it contains using interactive dashboards. In the current database release, we have curated 1347 papers, increasing the number of compounds to 36 545. In addition, we have initiated a large-scale effort to incorporate data from papers reporting structural reassignments and revisions to previously published structures. This effort led to the incorporation of 590 corrections to existing entries, significantly improving the accuracy of the dataset. The Natural Products Atlas may be accessed at www.npatlas.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 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)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.041
GPT teacher head0.337
Teacher spread0.295 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations81
Published2024
Admission routes2
Has abstractyes

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