通过根据经合组织标准开发的基于分类的机器学习模型,评估有机化学品在水和土壤环境中的生物降解潜力
Shubham Kumar Pandey1, Souvik Pore1, Kunal Roy1
1Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
The Science of the total environment
|September 3, 2025
概括
新的模型预测了水和土壤中的有机化学生物降解性. 生物降解工具通过选化学持久性和生物积累潜力来帮助环境风险评估和监管决策.
科学领域:
- 环境化学
- 计算毒理学
- 预测模型
背景情况:
- 评估有机化学生物降解对于了解环境持久性,生物积累和毒性至关重要.
- 现有的方法需要进行初步选,以便及早发现潜在的环境危害.
- 环境持续暴露于各种人为和自然来源的各种有机化学物质.
研究的目的:
- 开发和验证基于分类的c-RASBR模型,用于预测化学生物降解性.
- 评估机器学习算法的性能和SHapley添加式扩展 (SHAP) 功能的实用性.
- 创建一个易于使用的工具BiodegPred,用于预测水和土壤中的生物降解性.
主要方法:
- 使用经济合作与发展组织 (OECD) 测试指南数据集 (302和307) 应用交叉结构与属性关系 (RASPR) 方法.
- 对数据集2实施数据平衡技术,以减轻类别不平衡问题.
- 使用各种机器学习算法开发c-RASBR模型,包括支持矢量分类器 (SVC),并进行交叉验证 (CV) 和SHAP分析.
主要成果:
- 开发的c-RASBR模型,特别是使用SVC的模型,显示出高预测准确度 (训练组:0.730.98;测试组:0.770.84).
- SHAP分析证实了RASPR特征在模型预测中的重要性.
- 与传统的定性结构-生物降解性关系 (QSBR) 模型相比,具有较少特征的模型显示出更好的准确性.
结论:
- 开发的c-RASBR模型可靠地预测水中的有氧固有生物降解性和土壤中的生物转化潜力.
- 生物降解工具有效预测有机化学品的生物降解性,支持环境风险评估.
- 这些预测模型可以帮助监管机构做出有关化学安全和环境管理的明智决策.
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