title: Extending P450 site-of-metabolism models with region-resolution data.
abstract: BACKGROUND
  Cytochrome P450s are a family of enzymes responsible for the metabolism of approximately 90% of FDA-approved drugs. Medicinal chemists often want to know which atoms of a molecule-its metabolized sites-are oxidized by Cytochrome P450s in order to modify their metabolism. Consequently, there are several methods that use literature-derived, atom-resolution data to train models that can predict a molecule's sites of metabolism. There is, however, much more data available at a lower resolution, where the exact site of metabolism is not known, but the region of the molecule that is oxidized is known. Until now, no site-of-metabolism models made use of region-resolution data.
  
  
  RESULTS
  Here, we describe XenoSite-Region, the first reported method for training site-of-metabolism models with region-resolution data. Our approach uses the Expectation Maximization algorithm to train a site-of-metabolism model. Region-resolution metabolism data was simulated from a large site-of-metabolism dataset, containing 2000 molecules with 3400 metabolized and 30 000 un-metabolized sites and covering nine Cytochrome P450 isozymes. When training on the same molecules (but with only region-level information), we find that this approach yields models almost as accurate as models trained with atom-resolution data. Moreover, we find that atom-resolution trained models are more accurate when also trained with region-resolution data from additional molecules. Our approach, therefore, opens up a way to extend the applicable domain of site-of-metabolism models into larger regions of chemical space. This meets a critical need in drug development by tapping into underutilized data commonly available in most large drug companies.
  
  
  METHODS
  The algorithm, data and a web server are available at http://swami.wustl.edu/xregion.
authors.affiliation: Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63130, USA.
authors.forename: Jed M
authors.initials: JM
authors.lastname: Zaretzki
authors.affiliation: Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63130, USA.
authors.forename: Michael R
authors.initials: MR
authors.lastname: Browning
authors.affiliation: Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63130, USA.
authors.forename: Tyler B
authors.initials: TB
authors.lastname: Hughes
authors.affiliation: Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63130, USA.
authors.forename: S Joshua
authors.initials: SJ
authors.lastname: Swamidass
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003577:Cytochrome P-450 Enzyme System / Q000737:chemistry / Q000378:metabolism*; D006801:Humans; D008958:Models, Molecular; D062105:Molecular Docking Simulation; D054852:Small Molecule Libraries / Q000378:metabolism*; D013329:Structure-Activity Relationship; D015262:Xenobiotics / Q000378:metabolism*
chemical_list: D054852:Small Molecule Libraries; D015262:Xenobiotics; D003577:Cytochrome P-450 Enzyme System