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计算建模方法用于开发雌激素对抗性活动的协同效应预测模型
Myungwon Seo1, Jiwon Choi1, Jongseo Park1
1Chemical Analysis Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea.
预测化学混合物的健康风险是一项挑战. 这项研究开发了一种使用化学蛋白网络描述器的深度学习模型,以准确预测二元混合物中的协同雌激素效应.
科学领域:
- 环境化学环境化学
- 计算毒理学计算毒理学
- 生物技术是生物技术.
背景情况:
- 全球范围内,人们越来越担心雌激素内分泌破坏性化学物质 (EDC) 和它们的混合物对健康造成的风险.
- 传统的EDC实验性毒性测试是昂贵且耗时的.
- 现有的计算模型无法预测化学混合物的协同效应,因为它们只关注一种化学物质.
研究的目的:
- 开发一种计算方法来预测化学混合物的协同雌激素活性.
- 确定一个高性能预测模型,适用于评估混合物毒性,包括化学相互作用.
主要方法:
- 收集了二元混合物中雌激素活性的数据集,重点关注协同效应.
- 利用模型偏差比率方法将混合物分类为协同作用或非协同作用.
- 评估了五个分子描述符,四个机器学习算法和一个深度学习算法.
主要成果:
- 基于深度学习的算法与化学蛋白质网络描述器相结合,在预测协同效应方面表现出卓越的性能.
- 这种方法在准确性和效率方面优于其他评估的建模策略.
- 成功开发了一种高性能二进制分类模型.
结论:
- 开发了一种新的高性能二元分类模型,用于预测二元混合物中的协同雌激素效应.
- 该模型使用深度神经网络和基于化学蛋白质网络的描述器.
- 这种工具可以帮助在化学产品开发过程中初步选协同效应.
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