Message Passing Neural Networks Improve Prediction of Metabolite Authenticity.

ABSTRACT: Cytochrome P450 enzymes aid in the elimination of a preponderance of small molecule drugs, but can generate reactive metabolites that may adversely react with protein and DNA and prompt drug candidate attrition or market withdrawal. Previously developed models help understand how these enzymes modify molecule structure by predicting sites of metabolism or characterizing formation of metabolite-biomolecule adducts. However, the majority of reactive metabolites are formed by multiple metabolic steps, and understanding the progenitor molecule's network-level behavior necessitates an integrative approach that blends multiple site of metabolism and structure inference models. Our previously developed tool, XenoNet 1.0, generates metabolic networks, where nodes are molecules and weighted edges a

SUBMITTER: Flynn NR 

PROVIDER: S-EPMC10348819 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature