In silico tools to predict genotoxicity have become important for high-throughput screening of chemical substances. However, current in silico tools to evaluate chromosomal damage do not discriminate in vitro-specific positives that can be followed by in vivo tests. Herein, we establish an in silico model for chromosomal damages with the following approaches: (1) re-categorizing a previous data set into three groups (positives, negatives, and misleading positives) according to current reports that use weight-of-evidence approaches and expert judgments; (2) utilizing a generalized linear model (Elastic Net) that uses partial structures of chemicals (organic functional groups) as explanatory variables of the statistical model; and (3) interpreting mode of action in terms of chemical structures identified. The accuracy of our model was 85.6%, 80.3%, and 87.9% for positive, negative, and misleading positive predictions, respectively. Selected organic functional groups in the models for positive prediction were reported to induce genotoxicity via various modes of actions (e.g., DNA adduct formation), whereas those for misleading positives were not clearly related to genotoxicity (e.g., low pH, cytotoxicity induction). Therefore, the present model may contribute to high-throughput screening in material design or drug discovery to verify the relevance of estimated positives considering their mechanisms of action.

中文翻译：

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化学诱导染色体损伤的计算机模型阐明了作用方式和不相关的阳性结果

预测基因毒性的计算机工具对于化学物质的高通量筛选变得很重要。然而，目前用于评估染色体损伤的计算机模拟工具不能区分体外特异性阳性结果，这些阳性结果可以用于体内测试。在此，我们使用以下方法建立了染色体损伤的计算机模型：（1）根据当前使用证据权重方法的报告，将先前的数据集重新分类为三组（阳性、阴性和误导性阳性）和专家判断；(2) 利用广义线性模型(Elastic Net)，以化学品的部分结构(有机官能团)作为统计模型的解释变量；(3) 根据确定的化学结构解释作用模式。我们模型的准确率分别为 85.6%、80.3%、正面预测、负面预测和误导性正面预测分别为 87.9%。据报道，阳性预测模型中选定的有机官能团通过各种作用模式（例如，DNA 加合物形成）诱导基因毒性，而误导性阳性模型中的有机官能团与基因毒性（例如，低 pH、细胞毒性诱导）没有明确的相关性。因此，本模型可能有助于材料设计或药物发现中的高通量筛选，以验证考虑其作用机制的估计阳性的相关性。而那些误导性阳性结果与基因毒性（例如，低 pH 值、细胞毒性诱导）的关系并不明确。因此，本模型可能有助于材料设计或药物发现中的高通量筛选，以验证考虑其作用机制的估计阳性的相关性。而那些误导性阳性结果与基因毒性（例如，低 pH 值、细胞毒性诱导）的关系并不明确。因此，本模型可能有助于材料设计或药物发现中的高通量筛选，以验证考虑其作用机制的估计阳性的相关性。