Chemical design, preparation, agricultural bioefficacy valuation, and molecular docking of some pyridine containing compounds
Bibliographic record
Abstract
The development of effective insecticides is crucial for sustainable agriculture. This research focuses on a series of pyridine derivatives (3–9) that were prepared and evaluated for their agricultural bioefficacy as potential insecticides against cowpea aphid, Aphis craccivora Koch (Homoptera: Aphididae). The results demonstrate significant variations in bioefficacy among the tested compounds. Toxicity index analysis revealed the following order of insecticidal activity: 8>4>6>3>5>7>9, highlighting compound 8 as the most potent. Furthermore, potential binding interactions were elucidated through molecular docking studies between these compounds and relevant insect target proteins. So, the observed bioactivity trends were rationalized with the use of the docking data, which offered useful information on the binding affinities and molecular interactions. AChE, or acetylcholine esterase (PDB ID: 2ACE), has been docked against the seven synthetic molecules (3–9). Interestingly, compounds (2-(pyridin-2-ylthio)acetonitrile; 8), (2-(pyridin-2-ylthio)acetic acid; 6), and (ethyl 2-(pyridin-2-ylthio)acetate; 4) had the highest binding affinity, with respective docking scores (S) of -7.51, -7.45, and -7.12 kcal/mol, while compounds (thieno[2,3-b]pyridine derivatives; 7 and 9) had the lowest binding affinity (S=-6.52 and -6.73 kcal/mol, respectively). According to protein-ligand docking configurations, these compounds exhibited a range of binding interactions inside the 2ACE active site. Hence, this study contributes to the development of new pyridine-based insecticides for sustainable pest management in agricultural applications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".