Application of Fluorine- and Nitrogen-Walk Approaches: Defining the Structural and Functional Diversity of 2-Phenylindole Class of Cannabinoid 1 Receptor Positive Allosteric Modulators
Bibliographic record
Abstract
Cannabinoid 1 receptor (CB1R) allosteric ligands hold a far-reaching therapeutic promise. We report the application of fluoro- and nitrogen-walk approaches to enhance the drug-like properties of GAT211, a prototype CB1R allosteric agonist-positive allosteric modulator (ago-PAM). Several analogs exhibited improved functional potency (cAMP, β-arrestin 2), metabolic stability, and aqueous solubility. Two key analogs, GAT591 ( 6r ) and GAT593 ( 6s ), exhibited augmented allosteric-agonist and PAM activities in neuronal cultures, improved metabolic stability, and enhanced orthosteric agonist binding (CP55,940). Both analogs also exhibited good analgesic potency in the CFA inflammatory-pain model with longer duration of action over GAT211 while being devoid of adverse cannabimimetic effects. Another analog, GAT592 ( 9j ), exhibited moderate ago-PAM potency and improved aqueous solubility with therapeutic reduction of intraocular pressure in murine glaucoma models. The SAR findings and the enhanced allosteric activity in this class of allosteric modulators were accounted for in our recently developed computational model for CB1R allosteric activation and positive allosteric modulation.
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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) | 0.002 | 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".