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Record W4232782312 · doi:10.26434/chemrxiv.7791464

Development of Pyridine-Based Inhibitors for the Human Vaccinia-Related Kinases 1 and 2

2019· preprint· en· W4232782312 on OpenAlexaff
Ricardo A. M. Serafim, Fernando de Souza Gama, Caio dos Reis, Stanley N. S. Vasconcelos, André da Silva Santiago, Jéssica E. Takarada, Luiz Antônio Dutra, Fúlvia Di Pillo, Hátylas Azevedo, Alessandra Mascarello, Jonathan M. Elkins, Katlin B. Massirer, O. Gileadi, Cristiano R. W. Guimarães, Rafael M. Couñago

Bibliographic record

VenueChemRxiv · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiotin and Related Studies
Canadian institutionsStructural Genomics Consortium
Fundersnot available
KeywordsKinaseVacciniaPyridineChemistryStructure–activity relationshipStereochemistryBiochemistryIn vitroMedicinal chemistryRecombinant DNA

Abstract

fetched live from OpenAlex

Vaccinia-related kinases 1 and 2 (VRK1 and VRK2) are human Ser/Thr protein kinases associated with increased cell division and neurological disorders. Nevertheless, the cellular functions of these proteins are not fully understood. Despite their therapeutic potential, there are no inhibitors available for VRK1 or VRK2. We report here the discovery and elaboration of an aminopyridine scaffold as a basis for VRK1 and VRK2 inhibitors. The most potent compounds displayed KD values of 190 nM and 401 nM for VRK1 and VRK2, respectively. Differences in compound binding mode and substituent preferences between the two VRKs were identified by the series structure-activity relationship combined with the crystallographic analysis of key compounds. We expect that our results will serve as a starting point for the design of specific and potent inhibitors against each of the two VRKs based on a pyridine scaffold.

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 score0.002
Threshold uncertainty score0.006

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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