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XenoNet: Inference and Likelihood of Intermediate Metabolite Formation
dataset
posted on 2020-06-29, 19:07 authored by Noah R. Flynn, Na Le Dang, Michael D. Ward, S. Joshua SwamidassDrug metabolism is a common cause
of adverse drug reactions. Drug molecules can be metabolized into
reactive metabolites, which can conjugate to biomolecules, like protein
and DNA, in a process termed bioactivation. To mitigate adverse reactions
caused by bioactivation, both experimental and computational screening
assays are utilized. Experimental assays for assessing the formation
of reactive metabolites are low throughput and expensive to perform,
so they are often reserved until later stages of the drug development
pipeline when the drug candidate pools are already significantly narrowed.
In contrast, computational methods are high throughput and cheap to
perform to screen thousands to millions of compounds for potentially
toxic molecules during the early stages of the drug development pipeline.
Commonly used computational methods focus on detecting and structurally
characterizing reactive metabolite–biomolecule adducts or predicting
sites on a drug molecule that are liable to form reactive metabolites.
However, such methods are often only concerned with the structure
of the initial drug molecule or of the adduct formed when a biomolecule
conjugates to a reactive metabolite. Thus, these methods are likely
to miss intermediate metabolites that may lead to subsequent reactive
metabolite formation. To address these shortcomings, we create XenoNet,
a metabolic network predictor, that can take a pair of a substrate
and a target product as input and (1) enumerate pathways, or sequences
of intermediate metabolite structures, between the pair, and (2) compute
the likelihood of those pathways and intermediate metabolites. We
validate XenoNet on a large, chemically diverse data set of 17 054
metabolic networks built from a literature-derived reaction database.
Each metabolic network has a defined substrate molecule that has been
experimentally observed to undergo metabolism into a defined product
metabolite. XenoNet can predict experimentally observed pathways and
intermediate metabolites linking the input substrate and product pair
with a recall of 88 and 46%, respectively. Using likelihood scoring,
XenoNet also achieves a top-one pathway and intermediate metabolite
accuracy of 93.6 and 51.9%, respectively. We further validate XenoNet
against prior methods for metabolite prediction. XenoNet significantly
outperforms all prior methods across multiple metrics. XenoNet is
available at https://swami.wustl.edu/xenonet.