在中高性能液体染色学方法开发通过机器学习
Alberto Marchetto1,2, Monica Tirapelle1, Luca Mazzei1
1Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, U.K.
Analytical chemistry
|March 28, 2025
概括
本研究介绍了一种数据驱动的方法,用于预测高性能液态染色学 (HPLC) 中的分子保留,使用来自SMILES字符串的分子描述符. 这种方法加速了HPLC方法的开发,降低了成本和上市时间.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 高性能液态染色学 (HPLC) 对于分子分析和净化至关重要,但方法的开发是资源密集的.
- 数字化和不断增长的HPLC数据库需要高效的计算机辅助开发策略.
研究的目的:
- 根据移动相组合,开发数据驱动的方法来预测HPLC中的分子保留因子.
- 通过计算预测来减少HPLC方法开发中的实验工作的需要.
主要方法:
- 使用了从简化分子输入线输入系统 (SMILES) 字符串中衍生出的分子描述符 (MDs) 的定量结构-属性关系 (QSPR).
- 将MD集成到线性溶解能量关系 (LSER) 和线性溶剂强度 (LSS) 理论中.
- 使用现有的小分子实验保留因子数据验证了该方法.
主要成果:
- 在没有新的实验的情况下,成功地预测了作为移动相组成函数的溶液保留因子.
- 证明了该方法在直接预测化时间方面的潜力.
- 展示了在与机械运输模型相结合时优化in-silico HPLC 方法的可能性.
结论:
- 拟议的计算方法大大减少了实验工作量,并加速了HPLC方法的开发.
- 这种方法在制药生产中可节省大量的时间和成本,并加快市场投入时间.
- 预计随着高质量的HPLC数据日益可用,预测准确度将有所提高.
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