Understanding the underlying molecular and structural similarities between seemingly heterogeneous sets of drugs can aid in identifying drug repurposing opportunities and assist in the discovery of novel properties of preclinical small molecules. A wealth of information about drug and small molecule structure, targets, indications and side effects; induced gene expression signatures; and other attributes are publicly available through web-based tools, databases and repositories. By processing, abstracting and aggregating information from these resources into drug set libraries, knowledge about novel properties of drugs and small molecules can be systematically imputed with machine learning. In addition, drug set libraries can be used as the underlying database for drug set enrichment analysis. Here, we present Drugmonizome, a database with a search engine for querying annotated sets of drugs and small molecules for performing drug set enrichment analysis. Utilizing the data within Drugmonizome, we also developed Drugmonizome-ML. Drugmonizome-ML enables users to construct customized machine learning pipelines using the drug set libraries from Drugmonizome. To demonstrate the utility of Drugmonizome, drug sets from 12 independent SARS-CoV-2 in vitro screens were subjected to consensus enrichment analysis. Despite the low overlap among these 12 independent in vitro screens, we identified common biological processes critical for blocking viral replication. To demonstrate Drugmonizome-ML, we constructed a machine learning pipeline to predict whether approved and preclinical drugs may induce peripheral neuropathy as a potential side effect. Overall, the Drugmonizome and Drugmonizome-ML resources provide rich and diverse knowledge about drugs and small molecules for direct systems pharmacology applications. Database URL: https://maayanlab.cloud/drugmonizome/.

中文翻译：

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Drugmonizome 和 Drugmonizome-ML：用于药物富集分析和机器学习的小分子属性的集成和抽象


了解看似异质的药物之间潜在的分子和结构相似性有助于识别药物的再利用机会，并有助于发现临床前小分子的新特性。有关药物和小分子结构、靶点、适应症和副作用的丰富信息；诱导基因表达特征；和其他属性可通过基于网络的工具、数据库和存储库公开获得。通过处理、提取这些资源中的信息并将其聚合到药物组库中，可以通过机器学习系统地估算有关药物和小分子新特性的知识。此外，药物组库可以作为药物组富集分析的底层数据库。在这里，我们展示了 Drugmonizome，这是一个带有搜索引擎的数据库，用于查询带注释的药物和小分子集，以执行药物集富集分析。利用 Drugmonizome 中的数据，我们还开发了 Drugmonizome-ML。 Drugmonizome-ML 使用户能够使用 Drugmonizome 的药物集库构建定制的机器学习管道。为了证明 Drugmonizome 的实用性，对来自 12 个独立的 SARS-CoV-2 体外筛选的药物组进行了共识富集分析。尽管这 12 个独立的体外筛选之间的重叠度较低，但我们还是确定了对于阻止病毒复制至关重要的常见生物过程。为了演示 Drugmonizome-ML，我们构建了一个机器学习管道来预测已批准的临床前药物是否可能诱发周围神经病变作为潜在的副作用。 总体而言，Drugmonizome 和 Drugmonizome-ML 资源为直接系统药理学应用提供了有关药物和小分子的丰富多样的知识。数据库网址：https://maayanlab.cloud/drugmonizome/。