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Record W2900014623 · doi:10.1073/pnas.1813988115

Structure-guided development of selective M3 muscarinic acetylcholine receptor antagonists

2018· article· en· W2900014623 on OpenAlexaff
H Liu, Josefa Hofmann, I. Fish, Benjamin Schaake, Katrin Eitel, Amelie L. Bartuschat, Jonas Kaindl, Hannelore Rampp, Ashutosh Banerjee, Harald Hübner, Mary J. Clark, Sandra G. Vincent, John T. Fisher, Markus R. Heinrich, Kunio Hirata, Xiangyu Liu, Roger K. Sunahara, Brian K. Shoichet, Brian K. Kobilka, Peter Gmeiner

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsQueen's University
FundersNational Institute of General Medical Sciences
KeywordsMuscarinic acetylcholine receptorSelectivityAntagonistChemistryDocking (animal)Muscarinic antagonistIn vivoAcetylcholineReceptorBinding sitePharmacologyStereochemistryBiochemistryBiophysicsBiologyMedicineBiotechnology

Abstract

fetched live from OpenAlex

Drugs that treat chronic obstructive pulmonary disease by antagonizing the M3 muscarinic acetylcholine receptor (M3R) have had a significant effect on health, but can suffer from their lack of selectivity against the M2R subtype, which modulates heart rate. Beginning with the crystal structures of M2R and M3R, we exploited a single amino acid difference in their orthosteric binding pockets using molecular docking and structure-based design. The resulting M3R antagonists had up to 100-fold selectivity over M2R in affinity and over 1,000-fold selectivity in vivo. The crystal structure of the M3R-selective antagonist in complex with M3R corresponded closely to the docking-predicted geometry, providing a template for further optimization.

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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.034
GPT teacher head0.315
Teacher spread0.281 · 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

Citations96
Published2018
Admission routes1
Has abstractyes

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