Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model for direct prediction of the toxicity of a larger set of compounds, combing the application of an already predicted model for another species, and molecular descriptors.

We investigated a large acute toxicity data set of five aquatic organisms, fish, Daphnia magna, and algae from the VEGA-Hub, as well as Tetrahymena pyriformis and Vibrio fischeri.

The statistical quality of the models encouraged us to consider this alternative for the prediction of toxicity using interspecies extrapolation QSAAR models without regard to the toxicity mechanism or chemical class. In the case of algae, the use of activity values from a second species did not improve the models. This can be attributed to the weak interspecies relationships, due to different aquatic toxicity mechanisms in species.

回顾过去几十年来的毒理学文献，QSAR建模的关键要素一直是毒理作用和化学类别的机制。结果，通常很难在没有聚类化合物的情况下为特定终点设计一个可接受的单一模型。此处的主要目的是开发一种通过-通过定量结构-活性-活性关系（PP QSAAR）模型，用于直接预测更大的一组化合物的毒性，并结合已经预测的模型对另一物种和分子的应用。描述符。

我们调查了来自VEGA-Hub的五个水生生物，鱼类，大型蚤和藻类以及梨形四膜虫和费氏弧菌的五个大型急性毒性数据集。

模型的统计质量鼓励我们考虑使用种间外推QSAAR模型来预测毒性，而无需考虑毒性机理或化学类别。对于藻类，使用第二种物种的活性值并不能改善模型。这可能归因于物种间水生毒性机制不同，种间关系薄弱。