向着人工智能辅助的更绿色的Chiral HPLC:预测MP选择的高效电离分离-移动相 (EES-MP) 档案――A Lux纤维素-1案例研究
Carlos Pardo-Cortina1, Laura Escuder-Gilabert1, María José Medina-Hernández1
1Departamento de Química Analítica, Universitat de València, E-46100 Burjassot, Valencia, Spain.
Analytical chemistry
|December 22, 2025
概括
我们开发了一种新指标,即高效分离 (EES),并使用人工神经网络来预测性HPLC的最佳移动相. 这种方法可以减少试错,节省时间和资源.
科学领域:
- 分析化学 分析化学
- 染色体学 染色体学 是一种染色学.
- 计算化学计算化学
背景情况:
- 状高性能液体染色学 (HPLC) 方法的开发通常涉及广泛的试错查.
- 现有的方法缺乏优化移动相条件的预测能力.
研究的目的:
- 引入一种新型的指标,即高效分离 (EES),将分辨率和保留集成为全面的移动阶段建模.
- 开发和验证一个人工神经网络 (ANN) 共识模型,用于预测EES跨移动相组合在合HPLC.
- 为了使未来的可行性评估和智能选择移动阶段进行预先分离.
主要方法:
- 开发了EES参数,结合了分辨率 (Rs) 和保留 (k).
- 在Lux纤维素-1列上使用62个分子描述符和76个化合物训练多个人工神经网络 (ANN).
- 使用混乱竞争式学习优化器 (CCLNNA) 优化ANN并创建共识模型.
- 通过对素和洛米他泽的外部测试对模型的验证.
主要成果:
- 该ANN共识模型准确地复制了完整的EES移动阶段配置文件,其中R2> 0.9.9.
- 该模型显示出较低的误差分散,使得可靠的预测.
- 外部验证证实了对预测enantioseparability和识别最佳移动相的潜在实用性.
结论:
- 这项研究提出了第一个概念验证,用于在性HPLC中预测完整的EES移动相形状.
- 共识建模策略增强了稳定性,减少了实验力度,促进了更绿色的分析实践.
- 该框架是可扩展的,为可持续的性分离提供了更广泛应用的潜力.
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