Machine Learning Harnesses Molecular Dynamics to Discover New $\mu$ Opioid Chemotypes

Abstract

Computational chemists typically assay drug candidates by virtually screening compounds against crystal structures of a protein despite the fact that some targets, like the $\mu$ Opioid Receptor and other members of the GPCR family, traverse many non-crystallographic states. We discover new conformational states of $\mu OR$ with molecular dynamics simulation and then machine learn ligand-structure relationships to predict opioid ligand function. These artificial intelligence models identified a novel $\mu$ opioid chemotype.

28 pages, machine learning, computational biology, GPCRs, molecular dynamics, molecular docking, molecular simulation