实施多种物种毒性评估的全面机器学习模型,以改善有机化合物的监管
Ying He1, Guohong Liu2, Song Hu3
1Institute of Environmental Research at Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China.
Journal of hazardous materials
|June 30, 2023
概括
本研究引入了先进的机器学习模型,用于准确地预测化学水生物质的毒性,通过考虑多种物种和先进的算法来改进化学风险评估和监管.
科学领域:
- 环境化学环境化学
- 计算毒理学计算毒理学
- 生态毒理学 生态毒理学
背景情况:
- 目前用于化学危害评估的机器学习模型经常使用单个算法和毒性终点,可能导致偏见的化学法规.
- 评估水生有毒性对于理解生态风险和确保环境安全至关重要.
研究的目的:
- 开发使用先进的机器学习和深度学习来评估化学物质对水生有毒性的全面预测模型.
- 为各种有机化学品建立准确的定量结构-毒性关系 (QSTR).
- 为监管目的提供一个强大的工具来选大型化学库,并识别高风险化合物.
主要方法:
- 实施多种先进的机器学习算法和端到端深度学习模型.
- 开发模型来解开定量结构-毒性关系 (QSTRs).
- 通过整合多种物种的毒性数据来评估生态风险.
主要成果:
- 最佳模型在预测水生有毒性方面取得了很高的准确性,训练组的相关系数从0.59到0.81不等,测试组的系数从0.56到0.83.
- 该研究确定了物种敏感性作为关键的毒性机制,更高水平的生物体经历了更严重的影响.
- 选了超过16000种化合物,确定了许多高风险化学品.
结论:
- 开发的方法提供了一种可靠的方法来预测有机化学品的毒性.
- 这种工具可以帮助监管机构在有关化学品安全方面做出更明智和合理的决定.
- 这些发现突显了化学风险评估中多种和多算法方法的重要性.
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