IRAK inhibitor 6

For research use only. Not for therapeutic Use.

  • CAT Number: I000246
  • CAS Number: 1042672-97-8
  • Molecular Formula: C20H20N4O3S
  • Molecular Weight: 396.46
  • Purity: ≥95%
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IRAK inhibitor 6(Cat No.:I000246) is a selective Interleukin-1 receptor-associated kinase 4(IRAK4) inhibitor with IC50 of 0.16 μM. If you need, please contact us in time, and we will reply to you as soon as possible within 24 hours.


Catalog Number I000246
CAS Number 1042672-97-8
Synonyms

N-(2-methoxy-4-morpholin-4-ylphenyl)-2-pyridin-3-yl-1,3-thiazole-4-carboxamide

Molecular Formula C20H20N4O3S
Purity ≥95%
Target IRAK
Solubility 10 mM in DMSO
Storage Store at -20°C
IUPAC Name N-(2-methoxy-4-morpholin-4-ylphenyl)-2-pyridin-3-yl-1,3-thiazole-4-carboxamide
InChI InChI=1S/C20H20N4O3S/c1-26-18-11-15(24-7-9-27-10-8-24)4-5-16(18)22-19(25)17-13-28-20(23-17)14-3-2-6-21-12-14/h2-6,11-13H,7-10H2,1H3,(H,22,25)
InChIKey JQSRUVXPODZKAF-UHFFFAOYSA-N
SMILES COC1=C(C=CC(=C1)N2CCOCC2)NC(=O)C3=CSC(=N3)C4=CN=CC=C4
Reference

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<br>[1]. Quantitative structure-activity relationship (QSAR) study of interleukin-1 receptor associated kinase 4 (IRAK-4) inhibitor activity by the genetic algorithm and multiple linear regression (GA-MLR) method By Pourbasheer, Eslam; Riahi, Siavash; Ganjali, Mo December 2010, Vol. 25, No. 6 , Pages 844-853 (doi:10.3109/14756361003757893)
Abstract
A linear quantitative structure-activity relationship (QSAR) model is presented for the modelling and prediction for the interleukin-1 receptor associated kinase 4 (IRAK-4) inhibition activity of amides and imidazo[1,2-α] pyridines. The model was produced using the multiple linear regression (MLR) technique on a database that consisted of 65 recently discovered amides and imidazo[1,2- α] pyridines. Among the different constitutional, topological, geometrical, electrostatic and quantum-chemical descriptors that were considered as inputs to the model, seven variables were selected using the genetic algorithm subset selection method (GA). The accuracy of the proposed MLR model was illustrated using the following evaluation techniques: cross-validation, validation through an external test set, and Y-randomisation. The predictive ability of the model was found to be satisfactory and could be used for designing a similar group of compounds./
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