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Record W2059760579 · doi:10.1021/jm020164l

The Power of Visual Imagery in Drug Design. Isopavines as a New Class of Morphinomimetics and Their Human Opioid Receptor Binding Activity

2002· article· en· W2059760579 on OpenAlexaff
Stephen Hanessian, Saravanan Parthasarathy, Marc Mauduit, Kemal Payza

Bibliographic record

VenueJournal of Medicinal Chemistry · 2002
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsAstraZeneca (Canada)Université de Montréal
Fundersnot available
KeywordsChemistryEnantiopure drugStereochemistryAgonistLone pairOpioid receptorμ-opioid receptorTopology (electrical circuits)ReceptorMoleculeEnantioselective synthesisBiochemistryOrganic chemistryCombinatorics

Abstract

fetched live from OpenAlex

The importance of visual imagery and relational thinking manifests itself in a heuristic approach to the design and synthesis of potential morphinomimetics as agonists of the human mu receptor. The well-known class of alkaloids represented by the isopavine nucleus has a topological resemblance to the morphine skeleton, especially when viewed in a particular way. Enantiopure isopavines can be readily obtained from a 1,2 Stevens rearrangement of 13-substituted dihydromethanodibenzoazocines, prepared in four steps from d- and l-amino acids. Consideration of the topology and the expected orientation of the nitrogen lone pair for a better overlap with morphine necessitates the utilization of d-amino acids. By variation of the substituents on the aromatic rings and a judicious choice of ring substituents, it is possible to obtain low nanomolar binding to the human mu receptor while maintaining good to excellent mu/delta selectivity. Agonist-like activity is indicated in a functional assay for one of the analogues originally derived from d-alanine as a precursor. X-ray crystal structures of several compounds corroborate stereochemistries and overall topologies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.343
Teacher spread0.289 · 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 teacher head, 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

Citations48
Published2002
Admission routes1
Has abstractyes

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