Recent Progress in Metal‐Incorporated Acyclic Schiff‐Base Derivatives: Biological Aspects
Bibliographic record
Abstract
Abstract Schiff‐base derivatives are widely used organic compounds and their metal complexes have been drawing much attention to chemists due to interesting structural features and unique properties. The presence of nitrogen containing imine groups (−C=N−) in Schiff‐bases as well as in their metallic complexes and the chelating properties of these compounds are responsible for their many unique biological properties. In this review, we have summarized various acyclic Schiff‐base ligands and their different metallic complexes which have been reported in the last one decade. Schiff‐bases and their metal complexes have shown a broad range of biological activities, including antifungal, antibacterial, antimalarial, antiproliferative, anti‐inflammatory, antiviral, and antipyretic properties. We have reviewed and arranged the reported Schiff‐base derivatives according to their diverse biological applications, especially their anti‐bacterial and anti‐fungal activities, anti‐tumor and anti‐cancer activities, anti‐oxidant activities, anti‐inflammatory activities and other therapeutic and medicinal properties. The other component of this work as a review of the molecular structures of various Schiff‐bases since their molecular structures play a primary role in the biological properties. The presence of heteroatoms such as nitrogen, sulfur and oxygen with free electron pairs and aromatic rings in the structure of the Schiff‐bases play significant roles in determining their properties.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".