In silico evaluation of therapeutic potentials of Syringic acid against some selected diseases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the past few years several developments in medications have been made for the better treatment of certain diseases like Breast Cancer, Alzheimer’s disease, Tuberculosis, Obesity and Malaria. Phytochemicals possessing various medicinal properties have opened up the door to discover or design novel drug against these diseases. Syringic acid is such a natural compound found in many plants with a vast range of therapeutic potentials against several diseases. The present study aims to reveal Syringic acid as a potent inhibitor against Breast Cancer, Alzheimer’s disease, Tuberculosis, Obesity and Malaria comparing to the standard drugs of each disease. Molecular docking of syringic acid with critical proteins associated with the diseases was done using Schrödinger Maestro (v11.1). QikProp module of Schrödinger Maestro was used for ADME prediction and the toxicity of the ligand was evaluated by ProTox online databases. Syringic acid was found to exhibit acceptable ADME properties with no carcinogenicity and mutagenicity. Molecular docking result also showed higher scores compared to the commercially available standard drugs against four out of five diseases. The best docking scores were found against Breast cancer, Alzheimer’s disease, Obesity and Malaria which are -6.801 kcal/mol, -5.285 kcal/mol, -5.491 kcal/mol and -4.141 kcal/mol respectively. Syringic acid can be a stronger inhibitory potential agent against selected diseases than the standard drugs. Further clinical studies are required to consider syringic acid as an effective candidate drug for the better treatment of the mentioned diseases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it