Natural product-likeness score and its application for prioritization of compound libraries
Natural product-likeness score and its application for prioritization of compound libraries
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DOI：
10.1021/ci700286x
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发表时间：
2008-01-01
影响因子：
5.6
通讯作者：
Schuffenhauer, Ansgar
中科院分区：
文献类型：
--
作者：
Ertl, Peter;Roggo, Silvio;Schuffenhauer, Ansgar
天然产物（NP）已在很长的自然选择过程中进行了优化，以与生物大分子的最佳相互作用。因此，NP是新型生物活性分子设计的经过验证的子结构的极好来源。各种化学信息学技术可以为分析NP提供有用的帮助，并且在药物发现过程中可以使用此类研究的结果。在本研究中，我们描述了一种计算天然产品类评分的方法 - 贝叶斯措施，该方法可以确定分子与天然产物覆盖的结构空间的相似之处。在交叉验证实验中，该分数显示可有效地将NP与合成分子分开。在几个示例中讨论并说明了NP类似分数的可能应用，包括虚拟筛选，复合库的优先级别对NP类似性，以及用于合成NP类似NP的库的构建块的设计。
Natural products (NPs) have been optimized in a very long natural selection process for optimal interactions with biological macromolecules. NPs are therefore an excellent source of validated substructures for the design of novel bioactive molecules. Various cheminformatics techniques can provide useful help in analyzing NPs, and the results of such studies may be used with advantage in the drug discovery process. In the present study we describe a method to calculate the natural product-likeness score-a Bayesian measure which allows for the determination of how molecules are similar to the structural space covered by natural products. This score is shown to efficiently separate NPs from synthetic molecules in a cross-validation experiment. Possible applications of the NP-likeness score are discussed and illustrated on several examples including virtual screening, prioritization of compound libraries toward NP-likeness, and design of building blocks for the synthesis of NP-like libraries.