Rapid Screening and Prioritization of Culture Conditions for Natural Product Discovery using the Liquid Microjunction Surface Sampling Probe
Bibliographic record
Abstract
The discovery of novel bioactive compounds remains a cornerstone of natural product (NP) chemistry. However, a notable bottleneck in traditional NP discovery workflows is the extraction and purification of compounds of interest, a time and resource intensive process, which hinders sustainability and efficiency of novel hit discovery in multi-condition screening projects. This study evaluates the application of the liquid microjunction surface sampling probe (LMJ-SSP) with partial least squares discriminant analysis (PLS-DA) as a new step prior to the NP discovery workflow. By integrating ambient mass spectrometry with machine learning, we were able to analyze four strains of Penicillium fungi grown under thirteen unique conditions in situ, without sample preparation. By leveraging PLS-DA, the workflow prioritized the growth conditions that maximized chemical diversity, offering insights into metabolite composition before more resource intensive bulk growth and chromatographic methods are applied. The LMJ-SSP based approach also achieved a significant reduction in sampling time (96%), overall cost (98%), and solvent consumption (98%). Our triaging method efficiently streamlines the NP discovery pipeline through chemically-informed prioritization, reduced rediscovery of known compounds, and improved sustainability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".