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Record W2050971274 · doi:10.1021/jm901818u

1-(Aryl)-6-[alkoxyalkyl]-3-azabicyclo[3.1.0]hexanes and 6-(Aryl)-6-[alkoxyalkyl]-3-azabicyclo[3.1.0]hexanes: A New Series of Potent and Selective Triple Reuptake Inhibitors

2010· article· en· W2050971274 on OpenAlexaff
Fabrizio Micheli, Paolo Cavanni, Roberto Arban, Roberto Benedetti, Barbara Bertani, Michela Bettati, Letizia Bettelini, Giorgio Bonanomi, Simone Braggio, Anna Checchia, Silvia Davalli, Romano Di Fabio, Elettra Fazzolari, Stefano Fontana, Carla Marchioro, Doug Minick, Michele Negri, Beatrice Oliosi, Kevin D. Read, Ilaria Sartori, Giovanna Tedesco, Luca Tarsi, Silvia Terreni, Filippo Visentini, Alessandro Zocchi, Laura Zonzini

Bibliographic record

VenueJournal of Medicinal Chemistry · 2010
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsDiscovery Centre
Fundersnot available
KeywordsChemistryPharmacophoreArylStereochemistryIn vivoCombinatorial chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The discovery of new highly potent and selective triple reuptake inhibitors is reported. The new classes of 1-(aryl)-6-[alkoxyalkyl]-3-azabicyclo[3.1.0]hexanes and 6-(aryl)-6-[alkoxyalkyl]-3-azabicyclo[3.1.0]hexanes are described together with detailed SAR. Appropriate decoration of the scaffolds was achieved with the help of a triple reuptake inhibitor pharmacophore model detailed here. Selected derivatives showed good oral bioavailability (>30%) and brain penetration (B/B > 4) in rats associated with high in vitro potency and selectivity at SERT, NET, and DAT. Among these compounds, microdialysis and in vivo experiments confirm that derivative 15 has an appropriate developability profile to be considered for further progression.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.247
Teacher spread0.240 · 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

Citations51
Published2010
Admission routes1
Has abstractyes

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Same venueJournal of Medicinal ChemistrySame topicComputational Drug Discovery MethodsFrench-language works237,207