It is well-accepted that knowledge of a small molecule’s target can accelerate optimization. Although chemogenomic databases are helpful resources for predicting or finding compound interaction partners, they tend to be limited and poorly annotated. Furthermore, unlike genes, compound identifiers are often not standardized, and many synonyms may exist, especially in the biological literature, making batch analysis of compounds difficult. Here, we constructed an open-source annotation and target hypothesis prediction tool that explores some of the largest chemical and biological databases, mining these for both common name, synonyms, and structurally similar molecules. We used this Chemical Analysis and Clustering for Target Identification (CACTI) tool to analyze the Pathogen Box collection, an open-source set of 400 drug-like compounds active against a variety of microbial pathogens. Our analysis resulted in 4,315 new synonyms, 35,963 pieces of new information and target prediction hints for 58 members. Scientific contributions With the employment of this tool, a comprehensive report with known evidence, close analogs and drug-target prediction can be obtained for large-scale chemical libraries that will facilitate their evaluation and future target validation and optimization efforts.

中文翻译：

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CACTI：一种集成化学基因组数据和聚类分析的计算机化学分析工具


人们普遍认为，了解小分子的靶标可以加速优化。尽管化学基因组数据库是预测或寻找化合物相互作用伙伴的有用资源，但它们往往是有限的且注释不充分。此外，与基因不同，化合物标识符通常不标准化，并且可能存在许多同义词，尤其是在生物学文献中，这使得化合物的批量分析变得困难。在这里，我们构建了一个开源注释和目标假设预测工具，该工具探索一些最大的化学和生物数据库，挖掘这些数据库的通用名、同义词和结构相似的分子。我们使用这种用于目标识别的化学分析和聚类 (CACTI) 工具来分析 Pathogen Box 系列，这是一组开源的 400 种药物样化合物，对多种微生物病原体具有活性。我们的分析结果为 58 名成员提供了 4,315 个新同义词、35,963 条新信息和目标预测提示。科学贡献通过使用该工具，可以获得包含已知证据、密切类似物和药物靶点预测的大型化学库的综合报告，这将有助于其评估以及未来的靶点验证和优化工作。