A method for combining statistical-based QSAR predictions of two or more binary classification models is presented. It was assumed that all models were independent. This facilitated the combination of positive and negative predictions using a quantitative weight of evidence (qWoE) procedure based on Bayesian statistics and the additivity of the logarithms of the likelihood ratios. Previous studies combined more than one prediction but used arbitrary strengths for positive and negative predictions. In our approach, the combined models were validated by determining the sensitivity and specificity values, which are performance metrics that are a point of departure for obtaining values that measure the weight of evidence of positive and negative predictions. The developed method was experimentally applied in the prediction of Ames mutagenicity. The method achieved a similar accuracy to that of the experimental Ames test for this endpoint when the overall prediction was determined using a combination of the individual predictions of more than one model. Calculating the qWoE value would reduce the requirement for expert knowledge and decrease the subjectivity of the prediction. This method could be applied to other endpoints such as developmental toxicity and skin sensitisation with binary classification models.

中文翻译：

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定量证据权重方法，用于结合定量构效关系模型的预测。

提出了一种组合两个或多个二进制分类模型的基于统计的QSAR预测的方法。假定所有模型都是独立的。这使用了基于贝叶斯统计量的定量证据权重（qWoE）程序和似然比对数的可加性，促进了阳性和阴性预测的组合。先前的研究结合了多个预测，但对正负预测使用了任意强度。在我们的方法中，通过确定敏感性和特异性值来验证组合模型，这些敏感性和特异性值是性能指标，是获取可用来衡量阳性和阴性预测证据权重的值的起点。将该方法用于Ames诱变的预测。当使用多个模型的单个预测的组合确定总体预测时，该方法达到了与针对该终点的实验Ames测试相似的准确性。计算qWoE值将减少对专家知识的需求，并降低预测的主观性。该方法可应用于其他终点，例如具有二元分类模型的发育毒性和皮肤致敏性。