Chen Zhaoyang, Li Na, Li Ling, Liu Zihan, Zhao Wenqiang, Li Yan, Huang Xin, Li Xiao
Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China.
School of Pharmacy, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250014, China.
Environ Res. 2025 Jan 1;264(Pt 2):120356. doi: 10.1016/j.envres.2024.120356. Epub 2024 Nov 15.
Predicting the bioconcentration of chemical compounds plays a crucial role in assessing environmental risks and toxicological impacts. This study presents a robust multitask deep learning model for predicting the bioconcentration potential. The model can predict the bioconcentration of compounds in multiple categories, including non-bioconcentrative (non-BC), weakly bioconcentrative (weak-BC), and strongly bioconcentrative (strong-BC). We also employed the SHapley Additive exPlanations (SHAP) technology for the model interpretation. The binary classification models (non-BC vs BC and weak-BC vs strong-BC) showed good predictive performance, which achieved accuracy values over 90% and area under the curve (AUC) values with 0.95. The final ternary classification model provided an overall accuracy with 91.11%. Comparative analysis of molecular physicochemical properties showed that the importance of molecular weight, polar surface area, solubility, and hydrogen bonding are important for chemical bioconcentration. Besides, we identified eight structural alerts responsible for chemical bioconcentration. We made the model available as an online tool named BCdpi-predictor, which is accessible at http://bcdpi.sapredictor.cn/. Users can predict the bioconcentration potential of chemical compounds freely. The model has significant implications for environmental policy and regulatory frameworks, such as REACH, by providing a more accurate and interpretable method for assessing chemical risks. We hope that the results of this study can provide helpful tools and meaningful information for chemical bioconcentration prediction in environmental risk assessment.
预测化合物的生物富集在评估环境风险和毒理学影响方面起着至关重要的作用。本研究提出了一种强大的多任务深度学习模型来预测生物富集潜力。该模型可以预测多种类别化合物的生物富集情况，包括非生物富集性（non-BC）、弱生物富集性（weak-BC）和强生物富集性（strong-BC）。我们还采用了SHapley加性解释（SHAP）技术对模型进行解释。二元分类模型（non-BC与BC以及weak-BC与strong-BC）显示出良好的预测性能，准确率超过90%，曲线下面积（AUC）值为0.95。最终的三元分类模型的总体准确率为91.11%。分子物理化学性质的比较分析表明，分子量、极性表面积、溶解度和氢键的重要性对化学物质的生物富集很重要。此外，我们确定了八个与化学生物富集有关的结构警示。我们将该模型作为一个名为BCdpi-predictor的在线工具提供，可通过http://bcdpi.sapredictor.cn/访问。用户可以免费预测化合物的生物富集潜力。该模型通过提供一种更准确且可解释的化学风险评估方法，对环境政策和监管框架（如REACH）具有重要意义。我们希望本研究结果能够为环境风险评估中的化学生物富集预测提供有用的工具和有意义的信息。
