Message Passing Neural Networks Improve Prediction of Metabolite Authenticity

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 are metabolic transformations. We
extend XenoNet with a bidirectional message passing neural network
that integrates edge feature information and local network structure
using edge-conditioned graph convolutions and jumping knowledge to
predict the authenticity of inferred Phase I metabolite structures.
Our model significantly outperformed prior work and algorithmic baselines
on a data set of 311 networks and 6606 intermediates annotated using
a chemically diverse set of 20 736 individual in vitro and
in vivo reaction records accounting for 92.3% of all human Phase I
metabolism in the Accelrys Metabolite Database. Cross-validated predictions
resulted in area under the receiver operating characteristic curves
of 88.5% and 87.6% for separating experimentally observed and unobserved
metabolites at global and network levels, respectively. Further analysis
verified robustness to networks of varying depth and breadth, accurate
detection of metabolites, such as d,l-methamphetamine,
that are experimentally observed or unobserved in different network
contexts, extraction of important metabolic subnetworks, and identification
of known bioactivation pathways, such as for nimesulide and terbinafine.
By exploiting network structures, our approach accurately suggests
unreported metabolites for experimental study and may rationalize
modifications for avoiding deleterious pathways antecedent to reactive
metabolite formation.