用于化合物文库和组合化学空间多样性分析的生物活性分子基准集。

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL
Alexander Neumann*,  and , Raphael Klein, 
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引用次数: 0

摘要

商业上可获得的化合物的来源已经经历了几年的持续增长,在十亿到万亿规模的组合化学空间中达到了顶峰。为了评估化合物收集的质量以提供相关化学,需要一组药学相关结构的基准集,以便进行无偏比较。为此,对ChEMBL数据库中显示生物活性的分子进行了挖掘,并通过系统过滤和处理创建了三个连续数量级的基准集:Set L(“大尺寸”379k), Set M(“中型”25k)和Set S(“小尺寸”3k)。为了广泛覆盖物理化学和拓扑景观,基准Set S被用于分析商业组合化学空间和枚举化合物库的化学多样性能力。在使用的三种搜索方法中──FTrees(药效团特征)、SpaceLight(分子指纹)和SpaceMACS(最大共同子结构)──eXplore和REAL Space始终表现最好。一般来说,每个Chemical Space都能够提供比枚举库更多的与各自查询分子更相似的化合物,同时也为每种方法单独提供独特的支架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Benchmark Set of Bioactive Molecules for Diversity Analysis of Compound Libraries and Combinatorial Chemical Spaces

Sources for commercially available compounds have been experiencing continuous growth for several years, reaching their peak in billion- to trillion-sized combinatorial Chemical Spaces. To assess the quality of a compound collection to provide relevant chemistry, a benchmark set of pharmaceutically relevant structures is required that enables an unbiased comparison. For this purpose, the ChEMBL database was mined for molecules displaying biological activity, and three benchmark sets of successive orders of magnitude were created by systematic filtering and processing: Set L (“large-sized,” 379k), Set M (“medium-sized,” 25k), and Set S (“small-sized,” 3k). Tailored for broad coverage of the physicochemical and topological landscape, the benchmark Set S was then employed to analyze the chemical diversity capacities of commercial combinatorial Chemical Spaces and enumerated compound libraries. Among the three utilized search methods─FTrees (pharmacophore features), SpaceLight (molecular fingerprints), and SpaceMACS (maximum common substructure)─eXplore and REAL Space consistently performed best. In general, each Chemical Space was able to provide a larger number of compounds more similar to the respective query molecule than the enumerated libraries, while also individually offering unique scaffolds for each method.

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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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