QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary application

Bioconcentration factor (BCF) is one of the important parameters for developing human health ambient water quality criteria (HHAWQC) for chemical pollutants. Traditional experimental method to obtain BCF is time-consuming and costly. Therefore, prediction of BCF by modeling has attracted much attention. QSAR (Quanti-tative Structure-Activity Relationship) model based on molecular descriptor is often used to predict BCF, how-ever, in order to improve the accuracy of prediction, previous models are only applicable for prediction for a single category of substance and a single species, and cannot meet the needs of BCF prediction of pollutants lacing toxicity data. In this study, optimized 17 traditional molecular descriptor and five kinds of bioactivity descriptor were selected from more than 200 molecular descriptor and 25 kinds of biological activity descriptors. A QSAR-QSIIR (Quantitative Structure In vitro-In vivo Relationship) model suitable for multiple chemical sub-stances and whole species is constructed by using optimized 4-MLP machine learning algorithm with selected molecular and bioactivity descriptors. The constructed model significantly improves the prediction accuracy of BCF. The R2 of verification set and test set are 0.8575 and 0.7924, respectively, and the difference between predicted BCF and measured BCF is mostly less than 1.5 times. Then, BCF of BTEX in Chinese common aquatic products is predicted using the constructed QSAR-QSIIR model, and the HHAWQC of BTEX in China are derived using the predicted BCF, which provides a valuable reference for establishment of China's BTEX water quality standards.

Automated summary

Bioconcentration factor (BCF) is an important parameter for developing human health ambient water quality criteria (HHAWQC) for chemical pollutants. Traditional experimental methods are time-consuming and expensive, thus modeling approaches for predicting BCF have gained attention. However, previous models can only predict BCF for a single category of substance and species, limiting their applications. In this study, a QSAR-QSIIR model incorporating optimized molecular and bioactivity descriptors was constructed using the MLP machine learning algorithm, significantly improving BCF prediction accuracy.