一个基于深度学习的模拟器,用于全面的二维GC应用程序
Lucas Almir Cavalcante Minho1, Zenilda de Lourdes Cardeal1, Helvécio Costa Menezes1
1Departamento de Química, ICEx, Universidade Federal de Minas Gerais, Avenida Antônio Carlos, Belo Horizonte, Minas Gerais, Brazil.
深度学习模型在全面的二维气相色谱中准确预测保留时间,从而使方法优化和模拟成为可能. 建议建立一个协作数据库,以更好地预测不太常见的化合物.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
背景情况:
- 在全面的二维气相色谱 (GC×GC) 中预测化物位置对于方法优化至关重要.
- 深度学习 (DL) 模型为复杂的染色学数据分析提供了强大的和可适应的解决方案.
研究的目的:
- 为优化GC×GC方法开发一个开源的深度神经网络 (DNN) 程序.
- 为了实现操作条件的模拟和在实验室外预测保留时间.
主要方法:
- 使用实验预测器开发基于DNN的程序.
- 培训和验证DL模型用于预测第一维和第二维保留时间.
主要成果:
- 在第一维和第二维保留时间预测中,分别实现了0.006和0.014的缩放损失 (MSE).
- 对包括环境污染物,生物分子和制药在内的各种化学品类的预测准确度 (R2 > 0.8) 很好.
- 确定需要持续更新数据库以准确预测不太常见的化合物.
结论:
- DNN模型为GC×GC中的保留时间预测提供了可靠的方法.
- 开发的开源程序促进了方法优化和模拟.
- 建议建立一个协作数据库,以增强对更广泛化合物的预测能力.
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