Chemoinformatics Laboratory, UMR 7140 CNRS/University of Strasbourg, 4, rue Blaise Pascal, 67000, Strasbourg.
FSBSI "Chumakov FSC R&D IBP RAS", Poselok Instituta Poliomielita 8 bd. 1, Poselenie Moskovsky, Moscow, 108819, Russia.
Mol Inform. 2020 Dec;39(12):e2000080. doi: 10.1002/minf.202000080. Epub 2020 May 14.
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.
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之前针对相关物种的研究。科学家需要熟悉并对迄今为止经过测试的分子进行全球监督。化学地理学(此处特别使用生成拓扑映射)将结构放置在人类可读的 2D 图谱上(通过对分子描述符的化学空间进行降维获得),因此非常适合进行此类审核。目标是绘制迄今为止针对 CoV 进行药物化学研究的图谱。这包括比较针对各种病毒物种/属进行测试的文库,预测它们的多药理学特征,并突出经常遇到的化学型。图谱面临着针对病毒蛋白提供预测活性景观的挑战。“抗 CoV”图谱区域的定义导致从 8000 万种有机化合物的巨大池中选择了其中 380 种潜在的抗 CoV 药物。