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聚焦于表观遗传学靶点的合成筛选文库的化学信息学特征分析。

Chemoinformatic Characterization of Synthetic Screening Libraries Focused on Epigenetic Targets.

机构信息

DIFACQUIM Research Group, Department of Pharmacy, National Autonomous University of Mexico, Mexico City, 04510, Mexico.

Instituto de Quimica, National Autonomous University of Mexico, Mexico City, 04510, Mexico.

出版信息

Mol Inform. 2022 Jun;41(6):e2100285. doi: 10.1002/minf.202100285. Epub 2021 Dec 20.

Abstract

The importance of epigenetic drug and probe discovery is on the rise. This is not only paramount to identify and develop therapeutic treatments associated with epigenetic processes but also to understand the underlying epigenetic mechanisms involved in biological processes. To this end, chemical vendors have been developing synthetic compound libraries focused on epigenetic targets to increase the probabilities of identifying promising starting points for drug or probe candidates. However, the chemical contents of these data sets, the distribution of their physicochemical properties, and diversity remain unknown. To fill this gap and make this information available to the scientific community, we report a comprehensive analysis of eleven libraries focused on epigenetic targets containing more than 50,000 compounds. We used well-validated chemoinformatics approaches to characterize these sets, including novel methods such as automated detection of analog series and visual representations of the chemical space based on Constellation Plots and Chemical Library Networks. This work will guide the efforts of experimental groups working on high-throughput and medium-throughput screening of epigenetic-focused libraries. The outcome of this work can also be used as a reference to design and describe novel focused epigenetic libraries.

摘要

表观遗传药物和探针发现的重要性日益凸显。这不仅对于鉴定和开发与表观遗传过程相关的治疗方法至关重要,而且对于理解生物过程中涉及的潜在表观遗传机制也至关重要。为此,化学供应商一直在开发针对表观遗传靶点的合成化合物库,以增加鉴定有前途的药物或探针候选物的起点的可能性。然而,这些数据集的化学内容、其物理化学性质的分布和多样性仍然未知。为了填补这一空白并将这些信息提供给科学界,我们报告了对 11 个专注于表观遗传靶点的文库的综合分析,其中包含超过 50,000 种化合物。我们使用了经过充分验证的化学信息学方法来描述这些数据集,包括基于星座图和化学文库网络的自动检测类似物系列和化学空间的可视化表示等新方法。这项工作将指导从事高通量和中通量筛选表观遗传文库的实验组的工作。这项工作的结果也可以用作设计和描述新型聚焦于表观遗传学的文库的参考。

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