使用多目标QSAR方法预测杀虫剂的环境命运和毒性
1Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, 136119, India.
Chemistry & biodiversity
|December 18, 2023
概括
一个新的多目标定量结构-活性关系 (Mt-QSAR) 模型准确地预测了杀虫剂的环境命运和毒性. 该工具有助于评估新化学品,特别是那些缺乏实验数据的化学品.
科学领域:
- 环境化学环境化学
- 毒理学 毒理学 毒理学
- 计算化学是一种计算化学.
背景情况:
- 生态毒理风险评估对于工业化学品监管至关重要.
- 预测环境命运和毒性对于新的化学安全评估至关重要.
研究的目的:
- 开发一个强大的多目标定量结构-活性关系 (Mt-QSAR) 模型,同时预测杀虫剂的环境命运和毒性.
- 建立一个具有高外部可预测性的统计学上合理的模型,用于八醇-水分区系数 (LogP),生物度因子 (BCF) 和急性口服毒性 (LD50).
主要方法:
- 使用一个经过勒文伯格-马奎特 (LM) 算法训练的反向传播神经网络.
- 主要组件分析 (PCA) 与逐步多变量线性回归 (SW-MLR) 和人工神经网络 (ANN) 集成,用于模型构建 (SW-MLR-PCA-ANN).
- 模型性能使用RMSE,MPE,CCC,IIC和R2等指标以及域适用性 (AD) 评估进行了验证.
主要成果:
- 开发的Mt-QSAR模型表现出强大的内部 (RMSE=0.83,R2=0.69) 和外部验证 (RMSE=0.93,R2=0.61) 性能.
- 该模型实现了高的统计稳定性和可解释性,用于预测LogP,BCF,LD50rat和LD50bird.
- 成功预测了关键环境和毒性参数的缺失数据点.
结论:
- 提出的基于SW-MLR-PCA-ANN的Mt-QSAR方法提供了一种可靠的方法,用于同时预测杀虫剂的环境命运和毒性.
- 这种模型对于评估缺乏实验数据的新杀虫剂特别有价值,以支持监管决策.
- 该方法提高了对各种化学结构的生态毒理学风险评估的效率和准确性.
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