DR-Predictor: Incorporating Flexible Docking with Specialized Electronic Reactivity and Machine Learning Techniques to Predict CYP-Mediated Sites of Metabolism

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.