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Record W4205473916 · doi:10.26434/chemrxiv.13087742.v2

Design, Synthesis and Biological Evaluation of Novel SARS-CoV-2 3CLpro Covalent Inhibitors

2021· preprint· en· W4205473916 on OpenAlexafffund
Julia Stille, Jevgenijs Tjutrins, Guanyu Wang, Felipe A. Venegas, Christopher Hennecker, Andres Mauricio Rueda, Itai Sharon, Caitlin E. Miron, Sharon Pinus, Anne Labarre, Jessica Plescia, Mihai Burai Patrascu, Xiaocong Zhang, Alexander S. Wahba, Danielle Vlaho, Mitchell Huot, T.M. Schmeing, Anthony Mittermaier, Nicolas Moitessier

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldChemistry
TopicClick Chemistry and Applications
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsCovalent bondSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)VirologyCoronavirusViral replication2019-20 coronavirus outbreakDrug developmentPandemicFunction (biology)ChemistryBiologyOutbreakVirusPharmacologyMedicineDrugGenetics

Abstract

fetched live from OpenAlex

Severe diseases such as the ongoing COVID-19 pandemic, as well as the previous SARS and MERS outbreaks, are the result of coronavirus infections and have demonstrated the urgent need for antiviral drugs to combat these deadly viruses. Due to its essential role in viral replication and function, 3CL pro has been identified as a promising target for the development of antiviral drugs. Previously reported SARS-CoV 3CL pro non-covalent inhibitors were used as a starting point for the development of covalent inhibitors of SARS-CoV-2 3CL pro . We report herein our efforts in design and synthesis which led to submicromolar covalent inhibitors when the enzymatic activity of the viral protease was used as a screening platform.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.000
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 head0.192
GPT teacher head0.347
Teacher spread0.155 · 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.

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

Citations0
Published2021
Admission routes2
Has abstractyes

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