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Record W3011358496 · doi:10.21577/0103-5053.20200046

New Synthetic Quinolines as Cathepsin K Inhibitors

2020· article· en· W3011358496 on OpenAlexfundno aff
Taynara Lopes Silva, Aloisio de Andrade Bartolomeu, Hugo César Ramos de Jesus, Kléber T. de Oliveira, João Batista Fernandes, Dieter Brömme, Paulo C. Vieira

Bibliographic record

VenueJournal of the Brazilian Chemical Society · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsChemistryCombinatorial chemistryPharmacologyComputational biologyBiology

Abstract

fetched live from OpenAlex

Cathepsin K is a papain-like cysteine protease and is responsible for collagen degradation in bone tissue and thus represents an important target for the development of new therapies for treating diseases such as osteoporosis. Quinolines are an important class of heterocyclic molecular leads with a great pharmacological potential and represent a relevant scaffold to explore the chemical space of cathepsin K (CatK) inhibitors. This study presents the synthesis of nine 2,4-diphenylquinolines, including five phthalonitrile quinolines dyads, and the evaluation of their CatK inhibitory activity. Among the evaluated compounds, 4b was the most potent inhibitor with an IC 50 (half-maximal inhibitory concentration) value of 1.55 M (against Z-Phe-Arg-MCA substrate) acting in an uncompetitive inhibition mode. Molecular docking studies provided important information on the interaction of the inhibitor with the enzyme showing that these quinoline derivatives can play an important role as CatK inhibitors.

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 score0.060
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.007
GPT teacher head0.230
Teacher spread0.222 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Brazilian Chemical SocietySame topicBone Metabolism and DiseasesFrench-language works237,207