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DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active-state complexes and deep learning embeddings
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DOI：10.1016/j.molcel.2026.07.006.png)
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Abstract
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• AlphaFold has modest capability for prioritizing peptide agonists for GPCRs • Active-state receptor conformations enhance agonist discrimination • Pair representation subregions contain complementary predictive signals • Integration into a graph neural network “DeorphaNN” accelerates agonist discovery
Keywords:
G protein-coupled receptors
AlphaFold
deorphanization
peptide agonists
neuropeptides
peptide hormone
protein representations
active-state structures
protein embeddings
structural bioinformatics
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