The Impending Renaissance in Discovery & Development of Natural Products
Bibliographic record
Abstract
Antibiotics are wonder drugs. Unfortunately, owing to overuse, antibiotic resistance is now a serious problem. Society now finds itself in the post-antibiotic era, and the threat of infectious diseases is on the rise. New antibiotics are sorely needed. There is strong evidence that suggests natural products are an attractive source of new antimicrobials. They posses desirable structural and chemical properties that make them potent thearpeutics. However, steep tehnological challenges associated with screening and manufacturing these molecules has stifled the discovery, development and marketing of new antimicrobials. To this end, two recent scientific developments are poised to redress this situation. The recent development of metagenomics and ancillary high-throughput screening technologies has exponentiated the volume of useful genetic sequence information that can be screened for antimicrobial discovery. These approaches have been instrumental in the discovery of new antibiotics from soil and marine environments. Secondly, a new manufacturing paradigm employing metabolic engineering as its engine has greatly accelerated the path to market for these molecules, in addition to improving the atom and energy economy of antimicrobial manufacturing. We outine these developments in this review, and provide a perspective on integrating next-generation approaches such as metagenomics and metabolic engineering with traditional methodologies for discovering and manufacturing antimicrobial natural products in order to unleash a rennaissance in the discovery and development of antimicrobials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".