Insilico, network pharmacology and neural network studies on Sida acuta Burm f.- based phytochemicals targeting NADH-ubiquinone oxidoreductase
Bibliographic record
Abstract
The biochemical activities of phytochemicals obtained from S. acuta Burm f. have been described in several forms by various scientists globally. This work has revealed the antimalarial activities of S. acuta Burm f. using various methods such as density functional theory method, induced fit docking technique, quantitative structure activities relationship (QSAR) via neural network approach and pharmacokinetics studies using ADMETLab software. Compound F10 showed a stronger ability to interact in terms of HOMO energy and energy gap while Compound F12 exhibited a high tendency to accept electrons from nearby compounds. Furthermore, compound F13 demonstrated greater potential to inhibit NADH-Ubiquinone Oxidoreductase. We observed that the derivatives in F10, F12 and F13 enhanced the reactivity and inhibiting activities of the parent compound. The prediction of binding affinity for compounds F1 to F16 using neural network was found to be accurate and dependable. Also, ADMET study was executed and reported appropriately. Our research could pave the way for the creation of a library of effective phytochemicals from S. acuta Burm f -based drugs with potential anti-malaria activity.
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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".