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Record W4402583511 · doi:10.26434/chemrxiv-2024-091mt

Structure-Based Optimization and Biological Evaluation of SARS-CoV-2 3CLpro Covalent Inhibitors

2024· preprint· en· W4402583511 on OpenAlexafffund
Guanyu Wang, Julia Stille, Richard Boulon, Christopher Hennecker, Xiaocong Zhang, Nicole Blaine, Steven R. LaPlante, Anthony Mittermaier, Nicolas Moitessier

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldChemistry
TopicClick Chemistry and Applications
Canadian institutionsInstitut National de la Recherche ScientifiqueMcGill University
FundersCanadian Institutes of Health Research
KeywordsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)Computational biologyCovalent bond2019-20 coronavirus outbreakChemistryVirologyComputer scienceBiologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

The global emergence of COVID-19, caused by SARS-CoV-2, has underscored the critical need for effective antivirals against coronaviruses. The 3-chymotrypsin-like protease (3CLpro) of coronavirus has been a primary target for drug development due to its critical role across various coronaviruses. Following our initial report, this study focuses on the structure-based optimization of 3CLpro covalent inhibitors. With the guidance of molecular docking and covalent binding parameters measured from an innovative isothermal titration calorimetry-kinetic competition (ITC-KC) assay, we optimized and synthesized series of potent covalent inhibitors with antiviral activity in cell-based assays.

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 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 score0.048
Threshold uncertainty score1.000

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 integrity0.0010.001
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 head0.083
GPT teacher head0.339
Teacher spread0.256 · 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
Published2024
Admission routes2
Has abstractyes

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