As an alternative to one drug-one target approaches, systems biology methods can provide a deeper insight into the holistic effects of drugs. Network-based approaches are tools of systems biology, that can represent valuable methods for visualizing and analysing drug-protein and protein–protein interactions. In this study, a KNIME workflow is presented which connects drugs to causal target proteins and target proteins to their causal protein interactors. With the collected data, networks can be constructed for visualizing and interpreting the connections. The last part of the workflow provides a topological enrichment test for identifying relevant pathways and processes connected to the submitted data. The workflow is based on openly available databases and their web services. As a case study, compounds of DILIRank were analysed. DILIRank is the benchmark dataset for Drug-Induced Liver Injury by the FDA, where compounds are categorized by their likeliness of causing DILI. The study includes the drugs that are most likely to cause DILI (“mostDILI”) and the ones that are not likely to cause DILI (“noDILI”). After selecting the compounds of interest, down- and upregulated proteins connected to the mostDILI group were identified; furthermore, a liver-specific subset of those was created. The downregulated sub-list had considerably more entries, therefore, network and causal interactome were constructed and topological pathway enrichment analysis was performed with this list. The workflow identified proteins such as Prostaglandin G7H synthase 1 and UDP-glucuronosyltransferase 1A9 as key participants in the potential toxic events disclosing the possible mode of action. The topological network analysis resulted in pathways such as recycling of bile acids and salts and glucuronidation, indicating their involvement in DILI. The KNIME pipeline was built to support target and network-based approaches to analyse any sets of drug data and identify their target proteins, mode of actions and processes they are involved in. The fragments of the pipeline can be used separately or can be combined as required.

中文翻译：

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KNIME 工作流程，用于检索因果药物和蛋白质相互作用、构建网络以及执行 DILI 案例研究证明的拓扑富集分析


作为一种药物一种靶点方法的替代方法，系统生物学方法可以更深入地了解药物的整体效应。基于网络的方法是系统生物学的工具，可以代表可视化和分析药物-蛋白质和蛋白质-蛋白质相互作用的有价值的方法。在这项研究中，提出了一个 KNIME 工作流程，它将药物与因果目标蛋白以及目标蛋白与其因果蛋白相互作用物连接起来。利用收集到的数据，可以构建网络来可视化和解释这些连接。工作流程的最后一部分提供了拓扑富集测试，用于识别与提交的数据相关的相关路径和过程。该工作流程基于公开可用的数据库及其 Web 服务。作为案例研究，对 DILIRank 的化合物进行了分析。 DILIRank 是 FDA 药物性肝损伤的基准数据集，其中化合物根据引起 DILI 的可能性进行分类。该研究包括最有可能引起 DILI 的药物（“most DILI”）和不太可能引起 DILI 的药物（“no DILI”）。选择感兴趣的化合物后，鉴定出与大多数 DILI 组相关的下调和上调蛋白质；此外，还创建了其中的肝脏特异性子集。下调的子列表有相当多的条目，因此，构建了网络和因果相互作用组，并用该列表进行了拓扑路径富集分析。该工作流程将前列腺素 G7H 合酶 1 和 UDP-葡萄糖醛酸基转移酶 1A9 等蛋白质确定为潜在毒性事件的关键参与者，揭示了可能的作用模式。 拓扑网络分析得出胆汁酸和盐的回收以及葡萄糖醛酸化等途径，表明它们参与了 DILI。 KNIME 管道的构建是为了支持基于目标和网络的方法来分析任何药物数据集并识别其目标蛋白、作用模式和它们所涉及的过程。管道的片段可以单独使用，也可以组合为必需的。