MiMeDB 2.0: the Human Microbial Metabolome Database for 2026
Bibliographic record
Abstract
The Microbial Metabolome Database (MiMeDB) (https://mimedb.org) is a comprehensive, freely accessible resource linking human-associated microbes to the metabolites they produce, along with their connections to human health, disease, and diet. Since the release of MiMeDB 1.0 in 2023, the database has been substantially expanded and redesigned. Major updates include the systematic addition of millions of newly annotated genes and pathways, thousands of new metabolites, significantly expanded pathway and reaction coverage, along with broader representation of eukaryotic gut microbes. MiMeDB 2.0 now contains >12.9 million annotated microbial genes, over 23.1 million microbial pathways, 29 295 metabolites, 21 829 metabolic reactions, 3725 microbial species and strains, and 514 076 new experimental and predicted nuclear magnetic resonance and mass spectrometry spectra of microbial metabolites. New features, such as detailed microbial descriptions, metabolite origin tags, refined search filters, and species-specific reaction queries, have been added to enhance usability. Likewise, redesigned network and genome viewers have been implemented to support more comprehensive, intuitive, and integrated visualization of complex, multi-omic relationships. The significant addition of more metabolite spectral data and improved spectral search capabilities further strengthen metabolite identification and discovery. Together, these improvements make MiMeDB 2.0 one of the most comprehensive and user-friendly platforms for investigating the human microbiome at a molecular level and exploring the roles of microbes and microbial metabolites in human health, diet, and disease.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.053 |
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".