Integrating DNA-encoded chemical libraries with virtual combinatorial library screening: Optimizing a PARP10...

Area of Science:

• Medicinal Chemistry
• Drug Discovery and Development
• Computational Chemistry

Background:

• Drug development involves molecule discovery and lead optimization for potency and pharmacokinetics.
• DNA-encoded chemical libraries (DECLs) efficiently identify initial hits.
• Lead optimization is currently a bottleneck in translating DECL hits into viable drug candidates.

Purpose of the Study:

• To streamline the lead optimization process in drug development.
• To integrate DNA-encoded chemical library (DECL) screening with structure-based computational methods.
• To identify drug candidates with enhanced potency and druglike properties compared to initial DECL hits.

Main Methods:

• Enumeration of a virtual combinatorial library (VCL) based on a DECL screening hit.
• Application of computational binding prediction to assess virtual molecules.
• Identification of molecules with improved properties relative to the original DECL hit.

Main Results:

• A workflow combining DECL screening and computational prediction was successfully implemented.
• The approach identified molecules with enhanced properties compared to the initial hit.
• A more potent and druglike inhibitor of PARP10 was discovered as a proof-of-concept.

Conclusions:

• Integrating DECL screening with computational approaches accelerates lead compound development.
• This strategy effectively enhances molecule potency and druglike properties.
• The method provides a powerful tool for optimizing drug candidates derived from DECL screening.