Integrating DNA-encoded chemical libraries with virtual combinatorial library screening: Optimizing a PARP10 inhibitor. Issue 19 (1st October 2020)

• Record Type: Journal Article
• Authors: Lemke, Mike; Ravenscroft, Hannah; Rueb, Nicole J.; Kireev, Dmitri; Ferraris, Dana; Franzini, Raphael M.
• Is Part Of: Bioorganic & medicinal chemistry letters. Volume 30:Issue 19(2020)
• Publication Date: 2020-10-01
• Subjects: DNA-encoded chemical libraries -- Computer-guided drug discovery -- Poly-(ADP-ribose) polymerase -- Hit-to-lead development -- Virtual combinatorial libraries; Bioorganic chemistry -- Periodicals; Pharmaceutical chemistry -- Periodicals; 572
• DOI: 10.1016/j.bmcl.2020.127464
• Languages: English
• ISSNs: 0960-894X

Abstract

Graphical abstract: Abstract: Two critical steps in drug development are 1) the discovery of molecules that have the desired effects on a target, and 2) the optimization of such molecules into lead compounds with the required potency and pharmacokinetic properties for translation. DNA-encoded chemical libraries (DECLs) can nowadays yield hits with unprecedented ease, and lead-optimization is becoming the limiting step. Here we integrate DECL screening with structure-based computational methods to streamline the development of lead compounds. The presented workflow consists of enumerating a virtual combinatorial library (VCL) derived from a DECL screening hit and using computational binding prediction to identify molecules with enhanced properties relative to the original DECL hit. As proof-of-concept demonstration, we applied this approach to identify an inhibitor of PARP10 that is more potent and druglike than the original DECL screening hit.