Besalú Emili
Institut de Química Computacional i Catàlisi (IQCC) and Departament de Química, Universitat de Girona, 17071 Girona, Catalonia, Spain.
Int J Mol Sci. 2016 May 26;17(6):827. doi: 10.3390/ijms17060827.
The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties.
本文描述了叠加显著相互作用规则（SSIR）方法。它是一种通用的组合和符号程序，能够对属于组合类似物系列的化合物进行排名。该程序生成构效关系（SAR）模型，同时也可作为反向SAR工具。该方法速度快，可处理大型数据库。SSIR根据从可用化合物库中计算出的统计显著性，并依据先前附加的感兴趣或不感兴趣的分子标签进行操作。所需的符号编码允许处理几乎任何组合数据集，如有需要，甚至可以保密方式处理。应用示例将分子分类为结合或非结合，并通过训练以及两种不同的交叉验证方法（留一法和平衡留二法（BL2O））生成共识排名SAR模型，后者适用于二元性质的处理。
