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- W4317387729 abstract "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." @default.
- W4317387729 created "2023-01-19" @default.
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- W4317387729 date "2023-03-01" @default.
- W4317387729 modified "2023-10-14" @default.
- W4317387729 title "Targeting in silico GPCR conformations with ultra-large library screening for hit discovery" @default.
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- W4317387729 doi "https://doi.org/10.1016/j.tips.2022.12.006" @default.
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