Department of Analytical Resources, Moehs Ibérica S.L., Barcelona, Spain.
SAR QSAR Environ Res. 2020 Apr;31(4):261-279. doi: 10.1080/1062936X.2020.1725116. Epub 2020 Feb 17.
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.
本文提出了一种结合两个或多个二分类模型的基于统计学的定量构效关系（QSAR）预测的方法。假设所有模型都是独立的。这便于使用基于贝叶斯统计学的定量证据权重（qWoE）程序以及对数似然比的可加性来组合阳性和阴性预测。以前的研究结合了多个预测，但对阳性和阴性预测使用了任意强度。在我们的方法中，通过确定灵敏度和特异性值来验证组合模型，这些值是性能指标，是获得阳性和阴性预测证据权重值的起点。所开发的方法在预测 Ames 致突变性方面进行了实验应用。当使用多个模型的个体预测的组合来确定整体预测时，该方法在这个终点上达到了与实验 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，如发育毒性和皮肤致敏性与二分类模型。
