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Record W4410578141 · doi:10.1016/j.isci.2025.112706

How THC works: Explaining ligand affinity for, and partial agonism of, cannabinoid receptor 1

2025· article· en· W4410578141 on OpenAlexafffund
Farsheed Shahbazi‐Raz, Daniel Meister, Azam Mohammadzadeh, John F. Trant

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaUniversity of Windsor
KeywordsAgonismCannabinoidChemistryCannabinoid receptorLigand (biochemistry)ReceptorIntrinsic activityPartial agonistPharmacologyNeuroscienceStereochemistryPsychologyBiologyAntagonistBiochemistryPolitical scienceAgonist

Abstract

fetched live from OpenAlex

Interaction with cannabinoid receptor 1 (CB1) partially determines the bioactivity of the phytocannabinoids. Consequently, there has also been significant effort directed toward preparing synthetic cannabinoids with either enhanced agonistic or antagonistic activity against this receptor. The design process of these molecules, and the identification of off-target effects at this receptor for molecules designed to target other proteins, would be aided by a reliable computational tool that can accurately predict binding. Furthermore, although the mechanism of CB1 agonism is understood, the conformational behavior that underlies the molecular mechanism of partial agonism is unclear. In this report, we provide a correction for calculating a ligand's affinity to the orthosteric site of CB1 to account for their partition into membranes, use this to register the predicted affinity (high and low) of cannabinoids, and discuss how a mechanism for THC partial agonism arises natively from the model consistent with experimental data.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.023
GPT teacher head0.309
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2025
Admission routes2
Has abstractyes

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