Discovery of drugs against newly emerged pathogenic agents like the SARS‐CoV‐2 coronavirus (CoV) must be based on previous research against related species. Scientists need to get acquainted with and develop a global oversight over so‐far tested molecules. Chemography (herein used Generative Topographic Mapping, in particular) places structures on a human‐readable 2D map (obtained by dimensionality reduction of the chemical space of molecular descriptors) and is thus well suited for such an audit. The goal is to map medicinal chemistry efforts so far targeted against CoVs. This includes comparing libraries tested against various virus species/genera, predicting their polypharmacological profiles and highlighting often encountered chemotypes. Maps are challenged to provide predictive activity landscapes against viral proteins. Definition of “anti‐CoV” map zones led to selection of therein residing 380 potential anti‐CoV agents, out of a vast pool of 800 M organic compounds.

中文翻译：

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来自公共数据库 (ChEMBL) 的抗冠状病毒结构-活性信息的化学审计。

针对 SARS-CoV-2 冠状病毒 (CoV) 等新出现的病原体的药物发现必须基于先前对相关物种的研究。科学家们需要熟悉并对迄今经过测试的分子进行全球监督。化学（此处特别使用生成地形图）将结构放置在人类可读的二维地图上（通过分子描述符的化学空间的降维获得），因此非常适合此类审核。目标是绘制迄今为止针对 CoV 的药物化学工作图。这包括比较针对各种病毒种/属测试的文库，预测它们的多药理学特征并突出显示经常遇到的化学类型。地图面临着提供针对病毒蛋白的预测活动景观的挑战。