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Updated: Jun 10, 2026

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AquaticTox:基于集体学习的水生毒性评估网络工具,以促进对绿色化学品的查

Xing-Xing Shi¹, Zhi-Zheng Wang¹, Yu-Liang Wang¹

¹National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan 430079, China.

Environment & health (Washington, D.C.)

|October 30, 2024

概括

这项研究介绍了AquaticTox,一个使用组合模型预测水生生物中的有机化合物的急性毒性的网络服务器,为环保动物试验提供了更快,更具成本效益的替代方案.

科学领域:

环境毒理学环境毒理学
计算化学是一种计算化学.
生态毒理学生态毒理学

背景情况:

化学产品作为污染物对环境构成风险.
评估水生有毒性对于环境保护至关重要.
在生物体内对水生生物有毒性的动物测试是昂贵的,耗时的,并且在道德上具有挑战性.

研究的目的:

开发一种高效的in silico方法来预测水生物质的毒性.
创建一个用户友好的Web服务器,AquaticTox,用于毒性评估.
为了促进水生有毒化学品和绿色分子设计的识别.

主要方法:

基于六个基础学习者的集合模型的开发.
对五种水生生物的急性毒性预测:科林克斯 (Oncorhynchus mykiss),皮梅法莱斯 (Pimephales promelas),大夫尼亚 (Daphnia magna),伪科尼瑞拉 (Pseudokirchneriella subcapitata) 和甲 (Tetrahymena pyriformis).
整合结构-水生有毒作用模式 (MOA) 分析的知识库.

主要成果:

组合模型表现出比单个模型更优异的性能.
曲线下的实现面积 (AUC) 分数从0.75到0.92.9不等.
与最好的单个模型相比,平均精度提高了12-22%.

结论:

AquaticTox服务器提供了一个有效的in silico工具,用于预测水生物质的毒性.
整体模型在毒性评估中提供了更高的准确性和效率.
AquaticTox可以帮助识别危险化学品和设计环保分子.

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