Research on a Prediction Model for the Bioconcentration Factor (BCF) of Polyhalogenated Organic Phosphates Based on QSPR
The bioconcentration factor (BCF) is central to ecological-risk assessment, but experimental BCF measurement is too slow for large-scale chemical screening. Polyhalogenated organophosphate esters are widely used flame retardants and remain of concern because of their persistence and potential bioaccumulation. Here, we developed a quantitative structure–property relationship (QSPR) framework to predict BCF and support environmental risk prioritization for structurally related compounds. A dataset of 160 compounds was divided into training (n = 130) and test (n = 30) sets. Ten descriptors were selected from 766 candidates using a genetic algorithm. Multiple linear regression (MLR), support vector machine (SVM), and backpropagation artificial neural network (BP-ANN) models were constructed and evaluated. Dataset partitioning was examined using Tanimoto similarity analysis and uniform manifold approximation and projection (UMAP) visualization. Model performance was assessed using 11 validation metrics, bootstrap confidence intervals, and Williams-plot applicability-domain analysis. Among the three models, BP-ANN gave the lowest external prediction errors in the present test set (MAEtest = 0.33 and RMSEtest = 0.41) and Q2F2 = 0.79. This ranking should be interpreted cautiously because the test set contained 30 compounds. Descriptor interpretation suggests that BCF variation is associated with lipophilicity, molecular topology, electronic distribution, and phosphorus-containing functional groups. The framework may support early-tier screening of structurally related flame retardants within the defined applicability domain, but experimental confirmation remains necessary for regulatory decisions.