  Adverse Drug Reactions (ADRs) are characterized within randomized clinical
trials and postmarketing pharmacovigilance, but their molecular mechanism
remains unknown in most cases. Aside from clinical trials, many elements of
knowledge about drug ingredients are available in open-access knowledge graphs.
In addition, drug classifications that label drugs as either causative or not
for several ADRs, have been established. We propose to mine knowledge graphs
for identifying biomolecular features that may enable reproducing automatically
expert classifications that distinguish drug causative or not for a given type
of ADR. In an explainable AI perspective, we explore simple classification
techniques such as Decision Trees and Classification Rules because they provide
human-readable models, which explain the classification itself, but may also
provide elements of explanation for molecular mechanisms behind ADRs. In
summary, we mine a knowledge graph for features; we train classifiers at
distinguishing, drugs associated or not with ADRs; we isolate features that are
both efficient in reproducing expert classifications and interpretable by
experts (i.e., Gene Ontology terms, drug targets, or pathway names); and we
manually evaluate how they may be explanatory. Extracted features reproduce
with a good fidelity classifications of drugs causative or not for DILI and
SCAR. Experts fully agreed that 73% and 38% of the most discriminative features
are possibly explanatory for DILI and SCAR, respectively; and partially agreed
(2/3) for 90% and 77% of them. Knowledge graphs provide diverse features to
enable simple and explainable models to distinguish between drugs that are
causative or not for ADRs. In addition to explaining classifications, most
discriminative features appear to be good candidates for investigating ADR
mechanisms further.
