Automatised pharmacophoric deconvolution of plant extracts – application to <i>Cinchona</i> bark crude extract
Bibliographic record
Abstract
We present a development of the "Plasmodesma" dereplication method [Margueritte et al., Magn. Reson. Chem., 2018, 56, 469]. This method is based on the automatic acquisition of a standard set of NMR experiments from a medium sized set of samples differing by their bioactivity. From this raw data, an analysis pipeline is run and the data is analysed by leveraging machine learning approaches in order to extract the spectral fingerprints of the active compounds. The optimal conditions for the analysis are determined and tested on two different systems, a synthetic sample where a single active molecule is to be isolated and characterized, and a complex bioactive matrix with synergetic interactions between the components. The method allows the identification of the active compounds and performs a pharmacophoric deconvolution. The program is freely available on the Internet, with an interactive visualisation of the statistical analysis, at https://plasmodesma.igbmc.science.
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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 | 0.000 | 0.000 |
| 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".