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开发基于机器学习的模型,用于对非糖甜味剂的应用,进行突变性预测
Shilpayan Ghosh1, Vinay Kumar1, Kunal Roy1
1Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India.
Molecular informatics
|June 16, 2025
概括
机器学习模型预测了人工甜味剂的突变性,为体内试验提供了更快,更具成本效益的替代方案. 六种化合物被优先考虑为潜在的致病性非糖甜味剂 (NSS).
科学领域:
- 计算化学是一种计算化学.
- 毒理学 毒理学 毒理学
- 食品安全科学 食品安全科学
背景情况:
- 人工甜味剂,或非糖甜味剂 (NSSs),自第二次世界大战以来一直在使用.
- 有关NSS的突变性潜力存在担忧,需要对新化学品注册进行安全评估.
- 传统的体内突变性检测是耗时且昂贵的.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测NSSs的突变性.
- 为实验性突变性测试提供一个更有效的替代方案.
- 为了确定潜在的突变性NSS进行进一步的研究.
主要方法:
- 开发ML模型使用6881个有机化合物的数据集与随机数据分割 (50/50).
- 交叉验证分析来选择表现最佳的ML模型.
- 对332个NSS的共识预测,使用六个选定的模型,包括适用性领域评估.
- 与k-最近邻居的比较和毒性估计软件的预测.
主要成果:
- 根据严格的交叉验证,选择了六种ML模型.
- 在共识预测中,六种化合物被确定为潜在的突变性NSS.
- 与其他计算方法相比,模型衍生预测显示出可靠性.
结论:
- 机器学习模型为预测NSS变异性提供了一种可行和有效的方法.
- 该研究成功地优先考虑了六种化合物作为潜在的突变性NSSs.
- 已开发的模型可供公众使用,以协助食品安全评估.
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