Computational Macrocyclization: From de novo Macrocycle Generation to Binding Affinity Estimation. (25th October 2017)

• Record Type: Journal Article
• Authors: Wagner, Vincent; Jantz, Linda; Briem, Hans; Sommer, Kai; Rarey, Matthias; Christ, Clara D.
• Is Part Of: ChemMedChem. Volume 12:Number 22(2017)
• Page Start: 1866
• Page End: 1872
• Publication Date: 2017-10-25
• Subjects: drug design -- free energy calculations -- macrocycles -- molecular dynamics -- molecular modeling; Pharmaceutical chemistry -- Periodicals; 615.19005
• DOI: 10.1002/cmdc.201700478
• Languages: English
• ISSNs: 1860-7179

Abstract

Abstract: Macrocycles play an increasing role in drug discovery, but their synthesis is often demanding. Computational tools that suggest macrocyclization based on a known binding mode and that estimate the binding affinity of these macrocycles could have a substantial impact on the medicinal chemistry design process. For both tasks, we established a workflow with high practical value. For five diverse pharmaceutical targets we show that the effect of macrocyclization on binding can be calculated robustly and accurately. Applying this method to macrocycles designed by LigMac, a search tool for de novo macrocyclization, our results suggest that we have a robust protocol in hand to design macrocycles and prioritize them prior to synthesis. Abstract : Closing the loop : Macrocycles play an increasing role in drug discovery, but their synthesis is often demanding. For five targets we show that the effect of macrocyclization on binding can be calculated robustly and accurately. Applying the method to macrocycles designed by our tool LigMac, our results suggest that we have a robust protocol in hand to design and prioritize macrocycles prior to synthesis.