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用于分析和设计优化的小分子集合和库的 cheminformatics 工具。

Cheminformatics Tools for Analyzing and Designing Optimized Small-Molecule Collections and Libraries.

机构信息

HMS LINCS and Druggable Genome Centers, Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Warren Alpert 444, 200 Longwood Avenue, Boston, MA 02115, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.

Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45221, USA.

出版信息

Cell Chem Biol. 2019 May 16;26(5):765-777.e3. doi: 10.1016/j.chembiol.2019.02.018. Epub 2019 Apr 4.

Abstract

Libraries of well-annotated small molecules have many uses in chemical genetics, drug discovery, and therapeutic repurposing. Multiple libraries are available, but few data-driven approaches exist to compare them and design new libraries. We describe an approach to scoring and creating libraries based on binding selectivity, target coverage, and induced cellular phenotypes as well as chemical structure, stage of clinical development, and user preference. The approach, available via the online tool http://www.smallmoleculesuite.org, assembles sets of compounds with the lowest possible off-target overlap. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them and led us to design a new LSP-OptimalKinase library that outperforms existing collections in target coverage and compact size. We also describe a mechanism of action library that optimally covers 1,852 targets in the liganded genome. Our tools facilitate creation, analysis, and updates of both private and public compound collections.

摘要

小分子化合物库在化学遗传学、药物发现和治疗用途再利用方面有多种用途。有多种文库可供选择,但很少有数据驱动的方法可以对它们进行比较和设计新的文库。我们描述了一种基于结合选择性、目标覆盖度和诱导的细胞表型以及化学结构、临床开发阶段和用户偏好来评分和创建文库的方法。该方法可通过在线工具 http://www.smallmoleculesuite.org 使用,可组合出具有最低脱靶重叠的化合物集。使用我们的方法对六个激酶抑制剂文库进行分析,揭示了它们之间的巨大差异,并促使我们设计了一个新的 LSP-OptimalKinase 文库,在目标覆盖度和紧凑尺寸方面优于现有文库。我们还描述了一个作用机制文库,该文库可最佳地覆盖配体基因组中的 1852 个靶标。我们的工具可方便地创建、分析和更新私人和公共化合物库。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4141/6526536/e2fd156d306f/nihms-1523986-f0002.jpg

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本文引用的文献

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The target landscape of clinical kinase drugs.临床激酶药物的目标格局。
Science. 2017 Dec 1;358(6367). doi: 10.1126/science.aan4368.
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The ChEMBL database in 2017.2017年的ChEMBL数据库。
Nucleic Acids Res. 2017 Jan 4;45(D1):D945-D954. doi: 10.1093/nar/gkw1074. Epub 2016 Nov 28.

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