使用大规模机器学习建模对有机化合物的多终点急性毒性评估
Amirreza Daghighi1,2, Gerardo M Casanola-Martin1, Kweeni Iduoku1,2
1Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States.
Environmental science & technology
|May 27, 2024
概括
使用多条件描述符 (MCD) 的计算和机器学习模型可以准确预测化学毒性. 这种方法通过利用各种数据进行强大的定量结构-毒性关系 (QSTR) 模型来增强毒性测试.
科学领域:
- 计算毒理学计算毒理学
- 机器学习在药物发现中的作用
- 用于化学安全的预测建模.
背景情况:
- 毒性测试越来越依赖于替代方法,如计算和机器学习 (ML) 方法.
- 开发准确的预测模型受到复杂和稀缺的生物医学数据的阻碍.
- 多条件描述器 (MCD) 与非线性ML相结合,为整合多种测试数据提供了强大的解决方案.
研究的目的:
- 使用多条件描述器 (MCD) 开发一个定量结构-毒性关系 (QSTR) 模型.
- 评估单任务,多终点ML模型和卷积神经网络 (CNN) 的预测性能.
- 确定影响化合物毒性的关键结构特征.
主要方法:
- 将多条件描述符 (MCD) 应用于一个大数据集 (> 80,000 个化合物,59 个终点).
- 开发和比较单任务多端点ML模型.
- 使用卷积神经网络 (CNN) 进行新型数据分析方法.
主要成果:
- 使用MCD显著提高了模型性能.
- 与MCD相结合的CNN-1D模型实现了最好的预测准确性 (R2train = 0.93,R2ext = 0.70).
- 确定了与毒性相关的关键结构特征包括VSA,nN+,S-P碎片,电离潜力和C-N碎片.
结论:
- MCD 增强了 QSTR 模型的稳定性和准确性.
- 与MCD集成的CNN-1D模型为毒性预测提供了强大的工具.
- 开发的模型有助于快速评估新化学化合物的毒性.
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