Use of Chemical Genomics to Investigate the Mechanism of Action for Inhibitory Bioactive Natural Compounds
Bibliographic record
Abstract
One of the most significant hurdles in developing new drugs to treat diseases is understanding the specific mechanism(s) of action (MOA) by which small molecules discovered in cell-based screening exhibit their bioactivity. Natural products offer a nearly innumerable library of potential scaffolds for new drugs and have been a primary source for drug development. The process of characterizing the activity of natural products can be daunting. Traditional pathway-specific mechanistic approaches are time consuming and expensive. Genome scale assays provide a feasible alternative which offers a stepping stone in understanding an antimicrobial's mechanism of action by identifying pathways and genes/proteins whose endogenous activity is affected by the presence of an inhibitory natural compound. This chapter will discuss the use of genome-wide single-deletion arrays (GDAs) in Saccharomyces cerevisiae and Escherichia coli as well as combinatorial haploinsufficiency/homozygous mutant profiling (HIP/HOP) as genomic tools to investigate MOA in naturally derived inhibitory compounds.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".