Suppr超能文献

ECOSAR™ 中中性有机物对鱼类长期 QSAR 的内部和外部验证。

Internal and external validation of the long-term QSARs for neutral organics to fish from ECOSAR™.

机构信息

Chemical Risk Analysis, TNO Triskelion BV, Zeist, The Netherlands.

出版信息

SAR QSAR Environ Res. 2011 Jul-Sep;22(5-6):545-59. doi: 10.1080/1062936X.2011.569949. Epub 2011 Jul 7.

Abstract

This study concentrates on the external validation of an existing Quantitative Structure-Activity Relationship (QSAR) model widely used for long-term aquatic toxicity to fish. In the context of the REACH legislation, QSARs are used as an alternative for experimental data to achieve a complete environmental assessment without the need for animal testing. The predictivity of the model was evaluated in order to increase the reliability of the model. We assessed whether the model met all of the OECD principles. The model was adapted to become more robust, and predictions were made with an external validation set collected from several databases. For the internal validation of the QSAR, the r², Q²(Loo) and Q²(LMO) were used as validation criteria, and for the external validation r², Q²(ext), h and the validation ratio were used. A few substances were classified as outliers and therefore the applicability domain of the QSAR had to be adjusted. The QSAR passed all validation criteria and met all the OECD principles for QSAR validation, and the long-term toxicity QSAR for fish can be applied with high certainty of a correct prediction within the limits of the inherent uncertainty of the model in cases where the substance falls within the applicability domain.

摘要

本研究专注于广泛用于鱼类长期水生毒性的现有定量构效关系 (QSAR) 模型的外部验证。在 REACH 法规的背景下,QSAR 被用作替代实验数据的方法,以在无需动物测试的情况下实现完整的环境评估。为了提高模型的可靠性,评估了模型的预测能力。我们评估了模型是否符合所有 OECD 原则。该模型经过调整变得更加稳健,并使用从多个数据库中收集的外部验证集进行了预测。对于 QSAR 的内部验证,使用 r²、Q²(Loo) 和 Q²(LMO) 作为验证标准,对于外部验证,使用 r²、Q²(ext)、h 和验证比。有一些物质被归类为异常值,因此必须调整 QSAR 的适用域。QSAR 通过了所有验证标准,并符合 QSAR 验证的所有 OECD 原则,并且在物质属于适用域的情况下,该鱼类长期毒性 QSAR 可以在模型固有不确定性的限制内以很高的置信度正确预测。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验