The prediction of metabolism and biotransformation pathways
of xenobiotics is a highly desired tool in environmental and life
sciences. There are several systems that currently predict single
transformation steps or complete pathways as series of parallel
and subsequent steps. Their accuracy is often evaluated on the
level of a single transformation step. Such an approach cannot
account for some specific challenges that are related to the nature of the biotransformation experiments. This is particularly
true for missing transformation products in the reference data
that occur only in low concentrations, e.g. transient intermediates or higher-generation metabolites. Furthermore, some rulebased prediction systems evaluate accuracy only based on the
defined set of transformation rules. Therefore, the performance
of different models cannot be directly compared.
Data-driven extraction of relative reasoning rules to limit combinatorial explosion in biodegradation pathway prediction
