Something old, something new: revisiting natural products in antibiotic drug discovery
Bibliographic record
Abstract
Antibiotic discovery is in crisis. Despite a growing need for new drugs resulting from the increasing number of multi-antibiotic-resistant pathogens, there have been only a handful of new antibiotics approved for clinical use in the past 2 decades. Faced with scientific, economic, and regulatory challenges, the pharmaceutical sector seems unable to respond to what has been called an "apocalyptic" threat. Natural products produced by bacteria and fungi are genetically encoded products of natural selection that have been the mainstay sources of the antibiotics in current clinical use. The pharmaceutical industry has largely abandoned these compounds in favor of large libraries of synthetic molecules because of difficulties in identifying new natural product antibiotics scaffolds. Advances in next-generation genome sequencing, bioinformatics, and analytical chemistry are combining to overcome barriers to natural products. Coupled with new strategies in antibiotic discovery, including inhibition of resistance, novel drug combinations, and new targets, natural products are poised for a renaissance to address what is a pressing health care crisis.
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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".