The Antibiotic Resistome: A Guide for the Discovery of Natural Products as Antimicrobial Agents
Bibliographic record
Abstract
The use of life-saving antibiotics has long been plagued by the ability of pathogenic bacteria to acquire and develop an array of antibiotic resistance mechanisms. The sum of these resistance mechanisms, the antibiotic resistome, is a formidable threat to antibiotic discovery, development, and use. The study and understanding of the molecular mechanisms in the resistome provide the basis for traditional approaches to combat resistance, including semisynthetic modification of naturally occurring antibiotic scaffolds, the development of adjuvant therapies that overcome resistance mechanisms, and the total synthesis of new antibiotics and their analogues. Using two major classes of antibiotics, the aminoglycosides and tetracyclines as case studies, we review the success and limitations of these strategies when used to combat the many forms of resistance that have emerged toward natural product-based antibiotics specifically. Furthermore, we discuss the use of the resistome as a guide for the genomics-driven discovery of novel antimicrobials, which are essential to combat the growing number of emerging pathogens that are resistant to even the newest approved therapies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".