Targeting in silico GPCR conformations with ultra-large library screening for hit discovery

The use of deep machine learning (ML) in protein structure prediction has made it possible to easily access a large number of annotated conformations that can potentially compensate for missing experimental structures in structure-based drug discovery (SBDD). However, it is still unclear whether the accuracy of these predicted conformations is sufficient for screening chemical compounds that will effectively interact with a protein target for pharmacological purposes. In this opinion article, we examine the potential benefits and limitations of using state-annotated conformations for ultra-large library screening (ULLS) in light of the growing size of ultra-large libraries (ULLs). We believe that targeting different conformational states of common drug targets like G-protein-coupled receptors (GPCRs), which can regulate human physiology by switching between different conformations, can offer multiple advantages.

Automated summary

The use of deep machine learning in protein structure prediction allows easy access to annotated conformations, which can compensate for missing experimental structures in structure-based drug discovery. However, the accuracy of these predicted conformations for screening chemical compounds that effectively interact with protein targets is still uncertain. This opinion article examines the benefits and limitations of using state-annotated conformations for ultra-large library screening, particularly for common drug targets like G-protein-coupled receptors.