概念 DFT,机器学习和分子对接作为预测 LD 50 有机酸盐毒性的工具
Uriel J Rangel-Peña1, Luis A Zárate-Hernández1, Rosa L Camacho-Mendoza1
1Area Académica de Química, Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Hidalgo, Km. 4.5 Carretera Pachuca-Tulancingo, Ciudad del Conocimiento, C.P. 42184, Mineral de La Reforma, Hidalgo, México.
Journal of molecular modeling
|June 28, 2023
概括
机器学习模型,包括随机森林,准确预测有机酸盐毒性 (LD50). 这些模型利用量子化学的描述符,为化合物安全评估提供可靠的预测.
科学领域:
- 计算化学是一种计算化学.
- 毒理学 毒理学 毒理学
- 机器学习 机器学习
背景情况:
- 有机甲基酸盐化合物具有毒性风险.
- 预测毒性 (LD50) 对于安全性评估至关重要.
- 量子化学描述器为分子性质提供了洞察力.
研究的目的:
- 开发精确的有机酸盐毒性预测模型.
- 为了评估各种机器学习算法的性能.
- 确定影响毒性的关键分子描述因素.
主要方法:
- 使用DFT优化的分子结构 (ωB97XD/6-311++G**).
- 787个描述符使用Multiwfn,AIMALL和VMD.生成.
- 随机森林 (RF),LASSO,,弹性网 (EN) 和支向量机 (SVM) 应用.
- 使用AutoDock 4.2和LigPlot+进行的对接模拟.
主要成果:
- 随机森林模型 (A-RF-G1,A-RF-G2) 显示出高预测性能.
- 训练和测试组的R平方值大约为0.90.
- 在RF模型中确定了统计学意义上的参数.
结论:
- 机器学习模型,特别是射频,可以有效地预测有机甲基酸盐的毒性.
- cDFT和QTAIM描述符对于毒性预测有价值.
- 开发的模型为有机酸盐化合物的 in silico 选提供了基础.
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