<i>In Silico</i>-Guided Rational Drug Design and Synthesis of Novel 4-(Thiophen-2-yl)butanamides as Potent and Selective TRPV1 Agonists
Bibliographic record
Abstract
We describe an in silico -guided rational drug design and the synthesis of the suggested ligands, aimed at improving the TRPV1-ligand binding properties and the potency of N -(4-hydroxy-3-methoxybenzyl)-4-(thiophen-2-yl) butanamide I, a previously identified TRPV1 agonist. The docking experiments followed by molecular dynamics simulations and thermodynamic analysis led the drug design toward both the introduction of a lipophilic iodine and a flat pyridine/benzene at position 5 of the thiophene nucleus. Most of the synthesized compounds showed high TRPV1 efficacy and potency as well as selectivity. The molecular modeling analysis highlighted crucial hydrophobic interactions between Leu547 and the iodo-thiophene nucleus, as in amide 2a, or between Phe543 and the pyridinyl moiety, as in 3a . In the biological evaluation, both compounds showed protective properties against oxidative stress-induced ROS formation in human keratinocytes. Additionally, while 2a showed neuroprotective effects in both neurons and rat brain slices, 3a exhibited potent antinociceptive effect in vivo. .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".