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使用深度学习进行风险评估的新方法方法.

Enol Junquera¹, Irene Díaz¹, Susana Montes¹

¹University of Oviedo Oviedo Spain.

EFSA journal. European Food Safety Authority

|December 23, 2024

概括

此摘要是机器生成的。

人工智能 (AI) 通过使用分子相互作用预测化学毒性来推进风险评估,减少对动物试验的需求. 这种方法分析化学蛋白结合数据,以评估农药对人类和其他物种的影响.

关键词:

人工智能的人工智能是人工智能.分子对接的分子对接.分子压力因素是分子压力因素.农药的毒性农药的毒性蛋白质的3D结构风险评估风险评估风险评估软件开发软件开发软件开发

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科学领域:

计算毒理学计算毒理学
生物信息学是一种生物信息学.
风险评估中的人工智能

背景情况:

技术进步和人工智能使得大型生物数据集的快速处理成为可能.
越来越多的生物数据 (3D结构,交互网络) 支持新的风险评估方法.
人工智能 (AI) 提供预测能力作为新方法方法 (NAMs) 来彻底改变风险评估.

研究的目的:

开发一种基于人工智能的化学风险评估决策工具.
利用现有的毒性数据 (例如LD50) 和预测的化学蛋白相互作用.
支持对多种未经描述的压力因素的风险评估,重点关注农药对人类的影响.

主要方法:

利用人工智能分析大型生物和化学毒性数据集.
使用分子对接预测来评估化学蛋白结合亲和力.
从文献和技术报告中整合体内数据,以验证开发的NAM.

主要成果:

之前的研究发现了有毒化学物质与人体蛋白质 (神经,生殖功能) 的高亲和度结合.
确定了新类类药物与蜜蜂免疫系统蛋白质的潜在次致命相互作用.
建立了开发人工智能工具预测毒性和潜在致命影响的基础.

结论:

人工智能驱动的生物信息学方法可以显著影响毒性研究.
开发的NAM将指导实验设计,提高可预测性.
这些方法有望大幅减少对毒理学动物试验的依赖.