An in-silico approach of assessments of human serotonin transporter inhibitory potential of various flavonoids for anti-depressants: molecular docking, MM-GBSA, molecular dynamics simulation studies
Bibliographic record
Abstract
Abstract Depression is one of the typical CNS disorders. Millions of people suffer from depression, a chronic illness with economic consequences. Tricyclic antidepressants, selective dopamine reuptake inhibitors, selective norepinephrine reuptake inhibitors, and selective serotonin reuptake inhibitors are only few of the antidepressants that can used to treat depression. The main target of therapeutic activity is known to be the serotonin transporter (SERT) against depressants. In this article, various flavonoids were found with conditions of pharmacological activity and were designed by molecular docking, MM-GBSA and molecular dynamics (MD) Simulation studies for the treatment of depressants activity. The docking of ligands performed against depressant with protein of human serotonin transporter (SERT) PDB-ID:5I6X are performed by using Glide module, in silico ADMET screening by QikProp module, binding energy using Prime MMGB/SA module, MD simulation by Desmond module and atomic charges were derived by Jaguar module of Schrodinger suite 2021-1. Compounds with top binding affinity using extra precision in glide recorded as (-16.25) when compared to standard FDA approved drug Fluoxetine (-8.711) which were proposed for anti-depressant action. The residues PHE 335, TYR 95, ALA 96, PHE 341, VAL 501, TRP 103, TYR 175, ALA 169, GLY 338 of SERT Play a crucial role as binding pocket of ligands. The in-silico ADMET properties of the molecules were within the recommended values. The binding free energy was calculated using PRIME MM-GB/SA studies. Compound with top binding affinity of flavonoids was subjected to MD simulation at 100 ns to study the dynamic behavior of protein–ligand complex.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".