The Discovery of Novel Antimicrobial Agents by The Application of Isocyanide Based Multicomponent Reactions<strong> </strong>
Bibliographic record
Abstract
Multicomponent reactions (MCR) have been used to synthesis a wide range of analogs from several classes of heterocyclic compounds, with multifaceted medicinal uses. The synthesis of highly functionalized molecules in a single pot is a unique property of MCR, allowing researchers to quickly assemble libraries of compounds of biological interest and uncover novel leads as possible therapeutic agents. Isocyanide-based multicomponent reactions have proven to be extremely effective at swiftly specifying members of compound libraries, particularly in discovery of drug . The understanding of structure-activity correlations that drive the development of new goods and technology, requires structural variety in these libraries. In current world, antibiotic resistance is a major ongoing problem which is developing a problematic scenario in public health. The implementation of isocyanide based multicomponent reactions uphold a significant potential in this regard. By utilizing such reactions, new antimicrobial compounds can be discovered and fight against such concerns. This study discusses recent developments in antimicrobial medication discovery using isocyanide-based multicomponent reactions (IMCRs). Furthermore, the article emphasizes the potential of IMCRs in the near future.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".