Matched molecular series (MMS) analysis is an extension of matched molecular pair (MMP) analysis where all of the MMPs belong to the same chemical series. An MMS within a biological assay is able to capture specific structure activity relationships resulting from chemical substitution at a single location in the molecule. Under this convention, an MMS has the ability to capture one specific interaction vector between the compounds in a series and their therapeutic target. MMS analysis has the potential to translate the SAR from one series to another even across different protein targets or assays. A significant limitation of this approach is the lack of chemical series with a sufficient number of overlapping fragments to establish a statistically strong SAR in most databases. This results in either an inability to perform MMS analysis altogether or a potentially high proportion of spurious matches from chance correlations when the MMS compound count is low. This paper presents the novel concept of an MMS Network, which captures the SAR relationships between a set of related MMSs and significantly enhances the performance of MMS analysis by reducing the number of spurious matches leading to the identification of unexpected and potentially transferable SAR across assays. The results of a full retrospective leave-one-out analysis and randomization simulation are provided, and examples of pharmaceutically relevant programs will be presented to demonstrate the potential of this method.

中文翻译：

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匹配分子系列网络在跨靶结构活性关系转化和效能预测中的应用

匹配分子系列（MMS）分析是匹配分子对（MMP）分析的扩展，其中所有MMP都属于同一化学系列。生物测定中的MMS能够捕获由于分子中单个位置的化学取代而产生的特定结构活性关系。根据该惯例，MMS具有捕获一系列化合物与其治疗靶标之间的一个特定相互作用载体的能力。MMS分析具有将SAR从一个系列转换为另一个系列的潜力，甚至可以跨越不同的蛋白质靶标或测定法进行转换。该方法的显着局限性是缺乏化学序列，该化学序列缺乏足够数量的重叠片段，因此无法在大多数数据库中建立具有统计学意义的SAR。这会导致无法完全执行MMS分析，或者当MMS化合物计数较低时，可能会由于偶然性相关而导致很大比例的虚假匹配。本文提出了MMS网络的新颖概念，该网络捕获了一组相关MMS之间的SAR关系，并通过减少虚假匹配的数量导致了跨分析的意外和潜在可转移SAR的识别，从而显着增强了MMS分析的性能。提供了全面回顾性留一法分析和随机模拟的结果，并将提供一些与药物相关的程序示例，以证明该方法的潜力。本文提出了MMS网络的新颖概念，该网络捕获了一组相关MMS之间的SAR关系，并通过减少虚假匹配的数量导致了跨分析的意外和潜在可转移SAR的识别，从而显着增强了MMS分析的性能。提供了全面回顾性留一法分析和随机模拟的结果，并将提供一些与药物相关的程序示例，以证明该方法的潜力。本文提出了MMS网络的新颖概念，该网络捕获了一组相关MMS之间的SAR关系，并通过减少虚假匹配的数量导致了跨分析的意外和潜在可转移SAR的识别，从而显着增强了MMS分析的性能。提供了全面回顾性留一法分析和随机模拟的结果，并将提供一些与药物相关的程序示例，以证明该方法的潜力。