A mechanistic model explaining ligand affinity for, and partial agonism of, cannabinoid receptor 1
Bibliographic record
Abstract
CB1, a member of the G protein-coupled receptor class, is the putative protein target of THC, the psychoactive component of cannabis. To better identify new synthetic cannabinoids with increased activity, all cannabinoids with reported experimental binding to the CB1 receptor were modelled in silico to build a predictive model for CB1 affinity of small molecules. Computationally derived affinity is not sufficient in and of itself to predict binding, but coupled with the experimental evidence that ligands enter the receptor from the membrane rather than solvent, we provide a model that accurately describes the binding of these molecules by incorporating a correction factor for relative hydrophobicity. In addition, we propose a mechanism of action for partial CB1 agonists based on molecular dynamics simulations of THC homologues, modelling long time scale structural changes in the CB1 receptor. Together, the affinity model, and the mechanism of agonism/antagonism can allow for the computational prediction of both the effective behaviour and potency of novel cannabinoids, and several such predictions are made.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".