Both the acute toxicity and chronic toxicity data on aquatic organisms are indispensable parameters in the ecological risk assessment priority chemical screening process (e.g. persistent, bioaccumulative and toxic chemicals). However, most of the present modelling actions are focused on developing predictive models for the acute toxicity of chemicals to aquatic organisms. As regards chronic aquatic toxicity, considerable work is needed. The major objective of the present study was to construct in silico models for predicting chronic toxicity data for Daphnia magna and Pseudokirchneriella subcapitata. In the modelling, a set of chronic toxicity data was collected for D. magna (21 days no observed effect concentration (NOEC)) and P. subcapitata (72 h NOEC), respectively. Then, binary classification models were developed for D. magna and P. subcapitata by employing the k-nearest neighbour method (k-NN). The model assessment results indicated that the obtained optimum models had high accuracy, sensitivity and specificity. The model application domain was characterized by the Euclidean distance-based method. In the future, the data gap for other chemicals within the application domain on their chronic toxicity for D. magna and P. subcapitata could be filled using the models developed here.

水生生物的急性毒性和慢性毒性数据都是生态风险评估优先化学品筛选过程（例如持久性，生物蓄积性和有毒化学品）中必不可少的参数。但是，目前的大多数建模操作都集中在开发化学品对水生生物急性毒性的预测模型上。关于慢性水生毒性，需要大量工作。本研究的主要目的是建立预测大型蚤（Daphnia magna）和假拟假单胞菌（Pseudokirchneriella subcapitata）慢性毒性数据的计算机模型。在建模中，收集了D的一组慢性毒性数据。巨大（21天未观察到有效浓度（NOEC））和P。低于人均（72 h NOEC）。然后，通过采用k近邻法（k -NN），为D. magna和P. subcapitata开发了二元分类模型。模型评估结果表明，所获得的最优模型具有较高的准确性，敏感性和特异性。通过基于欧氏距离的方法对模型应用领域进行了表征。将来，可以使用此处开发的模型来填补应用领域内其他化学物质对大白僵菌和亚次生毕赤酵母的慢性毒性的数据缺口。