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Record W2967115979 · doi:10.1021/acs.jmedchem.9b00642

Integrated Synthetic, Biophysical, and Computational Investigations of Covalent Inhibitors of Prolyl Oligopeptidase and Fibroblast Activation Protein α

2019· article· en· W2967115979 on OpenAlexafffund
Jessica Plescia, Stéphane De Cesco, Mihai Burai Patrascu, Jerry Kurian, Justin M. Di Trani, Caroline Dufresne, Alexander S. Wahba, Naëla Janmamode, Anthony Mittermaier, Nicolas Moitessier

Bibliographic record

VenueJournal of Medicinal Chemistry · 2019
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesCanadian Institutes of Health ResearchMcGill University
KeywordsOligopeptidaseChemistryCovalent bondProteasesBiochemistrySerineEnzymeCombinatorial chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Over the past decade, there has been increasing interest in covalent inhibition as a drug design strategy. Our own interest in the development of prolyl oligopeptidase (POP) and fibroblast activation protein α (FAP) covalent inhibitors has led us to question whether these two serine proteases were equal in terms of their reactivity toward electrophilic warheads. To streamline such investigations, we exploited both computational and experimental methods to investigate the influence of different reactive groups on both potency and binding kinetics using both our own series of POP inhibitors and others' discovered hits. A direct correlation between inhibitor reactivity and residence time was demonstrated through quantum mechanics methods and further supported by experimental studies. This computational method was also successfully applied to FAP, as an overview of known FAP inhibitors confirmed our computational predictions that more reactive warheads (e.g., boronic acids) must be employed to inhibit FAP than for POP.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.014
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.236
Teacher spread0.227 · 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

Citations15
Published2019
Admission routes2
Has abstractyes

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