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.

中文翻译：

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通过知识图挖掘和可解释的AI研究ADR机制

在随机临床试验和上市后药物警戒中对药物不良反应（ADR）进行了表征，但在大多数情况下其分子机制仍然未知。除临床试验外，在开放获取知识图中还可以找到许多有关药物成分的知识。此外，已经建立了将药物标记为多种ADR的起因或非起因的药物分类。我们建议挖掘知识图以识别生物分子特征，这些特征可以使能够自动再现专家分类来区分给定类型的ADR是否是药物引起的。从可解释的AI角度来看，我们探索简单的分类技术，例如决策树和分类规则，因为它们提供了人类可读的模型，这些模型可以解释分类本身，但也可能提供ADR背后分子机制的解释要素。总而言之，我们为特征挖掘知识图。我们对分类人员进行与ADR相关或不相关的区分药物培训；我们分离出既能有效复制专家分类又能被专家解释的特征（例如，基因本体论术语，药物靶标或途径名称）；并且我们手动评估它们的解释方式。提取的特征以对DILI和SCAR有或没有致病药物的良好保真度分类进行再现。专家们完全同意，对于DILI和SCAR，最有区别的特征分别有73％和38％可以解释。并分别同意了其中的90％和77％（2/3）。知识图谱提供了多种功能，使简单而可解释的模型能够区分对ADR起因还是非起因的药物。除了解释分类之外，大多数区分特征似乎是进一步研究ADR机制的良好候选者。