Combining traditional 2D and modern physical organic-derived descriptors to predict enhanced enantioselectivity for the key aza-Michael conjugate addition in the synthesis of Prevymis™ (letermovir). Issue 34 (25th July 2018)

• Record Type: Journal Article
• Authors: Metsänen, Toni T.; Lexa, Katrina W.; Santiago, Celine B.; Chung, Cheol K.; Xu, Yingju; Liu, Zhijian; Humphrey, Guy R.; Ruck, Rebecca T.; Sherer, Edward C.; Sigman, Matthew S.
• Is Part Of: Chemical science. Volume 9:Issue 34(2018)
• Page Start: 6922
• Page End: 6927
• Publication Date: 2018-07-25
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/c8sc02089b
• Languages: English
• ISSNs: 2041-6520

Abstract

Abstract : Hybrid QSAR modeling predicts improved catalysts for the synthesis of Prevymis™ (letermovir). Abstract : Quantitative structure–activity relationships have an extensive history for optimizing drug candidates, yet they have only recently been applied in reaction development. In this report, the predictive power of multivariate parameterization has been explored toward the optimization of a catalyst promoting an aza-Michael conjugate addition for the asymmetric synthesis of letermovir. A hybrid approach combining 2D QSAR and modern 3D physical organic parameters performed better than either approach in isolation. Using these predictive models, a series of new catalysts were identified, which catalyzed the reaction to provide the desired product in improved enantioselectivity relative to the parent catalyst.