Applications of metabolimcs to medicinal plants for scientific study and drug discovery
Bibliographic record
Abstract
The term "metabolomics" was developed 10 years ago to describe the untargeted identification and quantification of all of the small metabolites in a biological sample. Over the last decade, researchers have been working to develop methodological approaches, standards, data analysis tools and new technologies that enable metabolome studies. Medicinal plants are ideal candidates for investigations by metabolomics as research questions often involve quantification multiple bioactive or marker compounds, synergy between molecules and complex interactions within a system. It has been estimated an average leaf sample could contain up to 30,000 compounds; the vast majority of which have never been isolated, identified or described. In metabolomic profiles we have observed 2,500 compounds in St. John's wort, 4,800 in Scutellaria baicalensis, and 5,200 in cranberries. By comparing metabolomes of different extracts and focusing the statistical analysis on unknowns in each sample, we have identified previously undiscovered compounds required for medicinal activity leading us to propose an alternate "Metabolomics-Based Drug Discovery Pipeline". Key factors for success are production of standardized active plant extracts and statistical methods to evaluate data quality thereby minimizing potential false discovery. This approach has the advantage of providing basic chemical information on compounds not sufficiently stable to withstand purification processes and could lead to a greater understandings of chemical synergy and the interactions of medicinal plants with human metabolism.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".