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arXiv · 1803.04479

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

Evan N. Feinberg·Amir Barati Farimani·Rajendra Uprety·Amanda Hunkele·Gavril W. Pasternak·Susruta Majumdar·Vijay S. Pande
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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.

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Evan N. Feinberg, Amir Barati Farimani, Rajendra Uprety, Amanda Hunkele, Gavril W. Pasternak, Susruta Majumdar, Vijay S. Pande. 2018-03-12. Machine Learning Harnesses Molecular Dynamics to Discover New $\mu$ Opioid Chemotypes. https://arxiv.org/abs/1803.04479

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