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Co-evolutions of correlations for QSAR of toxicity of organometallic and inorganic substances: An unexpected good prediction based on a model that seems untrustworthy

机译：有机金属和无机物质毒性QSAR相关性的共同演化：基于似乎不可信任的模型的意外良好预测

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The simplified molecular input line entry system (SMILES) gives a representation of the molecular structure by a sequence of special characters indicating different chemical elements, double/triple covalent bonds, and other features. We used this representation to establish quantitative structure-activity relationships (QSAR) for toxicity (pLD50, minus decimal logarithm of 50percent lethal dose) of organometallic and inorganic substances. The balance of correlations was used in the Monte Carlo optimization aimed to build up optimal descriptors. It should be noted, that there are few QSAR models in the literature which are dealing with organometallic and inorganic substances. We used CORAL (CORrelations And Logic) freeware, available on the Internet, for the modelling. Ten random splits into the sub-training, calibration, and test sets have been examined. Statistical characteristics of the model (for the split 1 ) are the following: n(velence)57, r~(2)(velence)0.6005, Q~(2)(velence)0.5721, s(velence)0.448, F(velence)83 (sub-training set); n(velence)55, r~(2)(velence)0.6005, R~(2)_(pred)velence0.5701, s(velence)0.501 (calibration set); n(velence)12, r~(2)velence0.8296, R~(2)_(pred)velence0.7695, and s(velence)0233 R_(m)~(2)velence0.8142 (test set). Statistical quality of models for other examined splits is also reasonable well.
机译：简化的分子输入线输入系统（SMILES）通过一系列特殊字符来表示分子结构，这些特殊字符指示不同的化学元素，双/三价共价键和其他特征。我们使用这种表示法建立了有机金属和无机物质毒性（pLD50，负数为50％致死剂量的十进制对数）的定量构效关系（QSAR）。相关性的平衡用于建立最佳描述符的蒙特卡洛优化中。应该注意的是，文献中很少涉及有机金属和无机物质的QSAR模型。我们使用Internet上可用的CORAL（关联和逻辑）免费软件进行建模。已检查了十个随机分组，分为子训练，校准和测试集。该模型的统计特征（对于拆分1）如下：n（velence）5​​7，r〜（2）（velence）0.6005，Q〜（2）（velence）0.5721，s（velence）0.448，F（velence） ）83（子训练集）; n（velence）5​​5，r〜（2）（velence）0.6005，R〜（2）_（pred）velence0.5701，s（velence）0.501（校准集）; n（velence）12，r〜（2）velence0.8296，R〜（2）_（pred）velence0.7695和s（velence）0233 R_（m）〜（2）velence0.8142（测试集）。其他检查的分割模型的统计质量也很合理。

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