Strategies for target identification of antimicrobial natural products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Covering: 2000 to 2015Despite a pervasive decline in natural product research at many pharmaceutical companies over the last two decades, natural products have undeniably been a prolific and unsurpassed source for new lead antibacterial compounds. Due to their inherent complexity, natural extracts face several hurdles in high-throughout discovery programs, including target identification. Target identification and validation is a crucial process for advancing hits through the discovery pipeline, but has remained a major bottleneck. In the case of natural products, extremely low yields and limited compound supply further impede the process. Here, we review the wealth of target identification strategies that have been proposed and implemented for the characterization of novel antibacterials. Traditionally, these have included genomic and biochemical-based approaches, which, in recent years, have been improved with modern-day technology and better honed for natural product discovery. Further, we discuss the more recent innovative approaches for uncovering the target of new antibacterial natural products, which have resulted from modern advances in chemical biology tools. Finally, we present unique screening platforms implemented to streamline the process of target identification. The different innovative methods to respond to the challenge of characterizing the mode of action for antibacterial natural products have cumulatively built useful frameworks that may advocate a renovated interest in natural product drug discovery programs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it