Machine Learning Harnesses Molecular Dynamics to Discover New $μ$ Opioid Chemotypes
This work addresses the challenge of drug discovery for GPCR targets like the μ opioid receptor, which have multiple non-crystallographic states, by combining computational methods to find new chemotypes.
The researchers tackled the problem of discovering new drug candidates for the μ opioid receptor by using molecular dynamics simulations to find new conformational states and machine learning to predict ligand function, resulting in the identification of a novel μ opioid chemotype.
Computational chemists typically assay drug candidates by virtually screening compounds against crystal structures of a protein despite the fact that some targets, like the $μ$ Opioid Receptor and other members of the GPCR family, traverse many non-crystallographic states. We discover new conformational states of $μOR$ with molecular dynamics simulation and then machine learn ligand-structure relationships to predict opioid ligand function. These artificial intelligence models identified a novel $μ$ opioid chemotype.