Safety & Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedford, MK44 1LQ, UK.
J Comput Aided Mol Des. 2012 Sep;26(9):1017-33. doi: 10.1007/s10822-012-9595-5. Epub 2012 Aug 24.
The bacterial reverse mutation assay (Ames test) is a biological assay used to assess the mutagenic potential of chemical compounds. In this paper approaches for the development of an in silico mutagenicity screening tool are described. Three individual in silico models, which cover both structure activity relationship methods (SARs) and quantitative structure activity relationship methods (QSARs), were built using three different modelling techniques: (1) an in-house alert model: which uses SAR approach where alerts are generated based on experts judgements; (2) a kNN approach (k-Nearest Neighbours), which is a QSAR model where a prediction is given based on outcomes of its k chemical neighbours; (3) a naive Bayesian model (NB), which is another QSAR model, where a prediction is derived using a Bayesian formula through preselected identified informative chemical features (e.g., physico-chemical, structural descriptors). These in silico models, were compared against two well-known alert models (DEREK and ToxTree) and also against three different consensus approaches (Categorical Bayesian Integration Approach (CBI), Partial Least Squares Discriminate Analysis (PLS-DA) and simple majority vote approach). By applying these integration methods on the validation sets it was shown that both integration models (PLS-DA and CBI) achieved better performance than any of the individual models or consensus obtained by simple majority rule. In conclusion, the recommendation of this paper is that when obtaining consensus predictions for Ames mutagenicity, approaches like PLS-DA or CBI should be the first choice for the integration as compared to a simple majority vote approach.
细菌回复突变检测（Ames 测试）是一种用于评估化学化合物致突变潜力的生物学检测方法。本文描述了开发一种计算机毒性筛选工具的方法。使用三种不同的建模技术构建了三个单独的计算机模型，涵盖了结构活性关系方法（SAR）和定量结构活性关系方法（QSAR）：（1）内部警示模型：使用 SAR 方法，根据专家判断生成警示；（2）kNN 方法（k-最近邻），这是一种 QSAR 模型，其中预测基于其 k 个化学邻的结果给出；（3）朴素贝叶斯模型（NB），这是另一种 QSAR 模型，其中通过预选的有信息量的化学特征（例如物理化学、结构描述符）使用贝叶斯公式进行预测。这些计算机模型与两种知名的警示模型（DEREK 和 ToxTree）进行了比较，还与三种不同的共识方法（分类贝叶斯集成方法（CBI）、偏最小二乘判别分析（PLS-DA）和简单多数投票方法）进行了比较。通过在验证集上应用这些集成方法，结果表明，两种集成模型（PLS-DA 和 CBI）的性能均优于任何单个模型或简单多数规则获得的共识。总之，本文建议在为 Ames 致突变性获得共识预测时，与简单多数投票方法相比，应优先选择 PLS-DA 或 CBI 等方法进行集成。
