title: DR-predictor: incorporating flexible docking with specialized electronic reactivity and machine learning techniques to predict CYP-mediated sites of metabolism.
abstract: Computational methods that can identify CYP-mediated sites of metabolism (SOMs) of drug-like compounds have become required tools for early stage lead optimization. In recent years, methods that combine CYP binding site features with CYP/ligand binding information have been sought in order to increase the prediction accuracy of such hybrid models over those that use only one representation. Two challenges that any hybrid ligand/structure-based method must overcome are (1) identification of the best binding pose for a specific ligand with a given CYP and (2) appropriately incorporating the results of docking with ligand reactivity. To address these challenges we have created Docking-Regioselectivity-Predictor (DR-Predictor)--a method that incorporates flexible docking-derived information with specialized electronic reactivity and multiple-instance-learning methods to predict CYP-mediated SOMs. In this study, the hybrid ligand-structure-based DR-Predictor method was tested on substrate sets for CYP 1A2 and CYP 2A6. For these data, the DR-Predictor model was found to identify the experimentally observed SOM within the top two predicted rank-positions for 86% of the 261 1A2 substrates and 83% of the 100 2A6 substrates. Given the accuracy and extendibility of the DR-Predictor method, we anticipate that it will further facilitate the prediction of CYP metabolism liabilities and aid in in-silico ADMET assessment of novel structures.
authors.affiliation: Department of Chemistry and Chemical Biology and §Department of Mathematics, Rensselaer Polytechnic Institute , Troy, New York 12180, United States.
authors.forename: Tao-wei
authors.initials: TW
authors.lastname: Huang
authors.forename: Jed
authors.initials: J
authors.lastname: Zaretzki
authors.forename: Charles
authors.initials: C
authors.lastname: Bergeron
authors.forename: Kristin P
authors.initials: KP
authors.lastname: Bennett
authors.forename: Curt M
authors.initials: CM
authors.lastname: Breneman
mesh_terms: D001185:Artificial Intelligence; D001189:Aryl Hydrocarbon Hydroxylases / Q000737:chemistry* / Q000378:metabolism; D001711:Biotransformation; D020134:Catalytic Domain; D019388:Cytochrome P-450 CYP1A2 / Q000737:chemistry* / Q000378:metabolism; D065637:Cytochrome P-450 CYP2A6; D006801:Humans; D006860:Hydrogen Bonding; D057927:Hydrophobic and Hydrophilic Interactions; D008024:Ligands; D062105:Molecular Docking Simulation; D011485:Protein Binding; D054852:Small Molecule Libraries / Q000737:chemistry* / Q000378:metabolism; D013329:Structure-Activity Relationship; D013379:Substrate Specificity; D013816:Thermodynamics
chemical_list: D008024:Ligands; D054852:Small Molecule Libraries; D001189:Aryl Hydrocarbon Hydroxylases; C504573:CYP1A2 protein, human; D019388:Cytochrome P-450 CYP1A2; D065637:Cytochrome P-450 CYP2A6