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Site of Metabolism Prediction Based on ab initio Derived Atom Representations
Author(s) -
Finkelmann Arndt R.,
Göller Andreas H.,
Schneider Gisbert
Publication year - 2017
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.201700097
Subject(s) - atom (system on chip) , artificial intelligence , machine learning , computer science , ab initio , steric effects , quantum chemistry , reactivity (psychology) , chemistry , computational chemistry , biological system , molecule , stereochemistry , biology , medicine , supramolecular chemistry , alternative medicine , organic chemistry , pathology , embedded system
Machine learning models for site of metabolism (SoM) prediction offer the ability to identify metabolic soft spots in low‐molecular‐weight drug molecules at low computational cost and enable data‐based reactivity prediction. SoM prediction is an atom classification problem. Successful construction of machine learning models requires atom representations that capture the reactivity‐determining features of a potential reaction site. We have developed a descriptor scheme that characterizes an atom's steric and electronic environment and its relative location in the molecular structure. The partial charge distributions were obtained from fast quantum mechanical calculations. We successfully trained machine learning classifiers on curated cytochrome P450 metabolism data. The models based on the new atom descriptors showed sustained accuracy for retrospective analyses of metabolism optimization campaigns and lead optimization projects from Bayer Pharmaceuticals. The results obtained demonstrate the practicality of quantum‐chemistry‐supported machine learning models for hit‐to‐lead optimization.