In-silico INVESTIGATION OF FERULIC ACID DERIVATES AGAINST MAIN PROTEASE SARS-COV
Why this work is in the frame
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Bibliographic record
Abstract
Ferulic acid is one of the natural compounds which is prevalent in various plants. This compound has known to possess extensive biological activity to get good health and well-being. In this study, we designed 23 derivates of ferulic acid and evaluate their potency in silico as potential SARS-CoV Mpro inhibitors. Furthermore, in silico ADME profiles of designed compounds were evaluated to verify whether the ferulic acid analogs possess an acceptable pharmacokinetic profile. The molecular docking result using AutoDock 4.2.6 showed that compound FA-24, which contained dihydro benzoxazine moiety, possesses a better docking score among the designed compound. Five top compounds based on docking score (FA-16, FA-17, FA-18, FA-23, and FA-24) were then evaluated using molecular dynamics for 10 ns, followed by free binding energy evaluation using the MM-PBSA approach. The result indicated that all compounds formed stable complexes with the enzyme for 100 ns. However, MM-PBSA result showed that compound FA-16, which contained phenyl benzoate moiety, possess higher free binding energy. It is argued that this difference was due to the nature of free binding energy evaluation, which was based on molecular dynamics results. Although, both the docking score and free binding energy of the designed compound are lower than the native ligand (AZP), it is believed that further structure modification could be performed to address this shortcoming. Ultimately, all designed ferulic acid analogs possess optimal absorption and drug-likeness characteristic, while several compounds were predicted to interact with isoforms of CYP450.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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