开发一种高效和通用的MTSCAM模型,以预测有机化合物的液体染色学保留时间
Mengdie Fan1,2, Chenhui Sang2,3, Hua Li4
1National Key Laboratory of Veterinary Public Health Security, College of Veterinary Medicine, China Agricultural University, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, and Beijing Laboratory for Food Quality and Safety, Beijing 100193, China.
一个新的机器学习模型使用新型分类系统和数据增强精确预测液体染色学保留时间. 这种高通量方法可以提高非目标选应用的预测准确度和错误率.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 化学信息学 化学信息学
背景情况:
- 准确预测液体色谱保留时间对于非目标查至关重要.
- 基于标准的传统方法是缓慢且劳动密集的.
- 现有的机器学习模型与数据稀缺性和不良概括性作斗争.
研究的目的:
- 开发一种新的,高通量保留时间预测模型.
- 克服现有预测方法的局限性,包括数据稀缺和缺乏分类.
- 为了提高预测未知化合物的保留时间的准确性和概括能力.
主要方法:
- 构建了一个由10905种化合物和保留时间组成的数据集.
- 实施了基于功能组权重的化合物的三级层次分类系统.
- 使用SMILES计数和结构相似度扩展的应用数据增强.
- 训练了每个类别的定量结构-保留关系 (QSRR) 模型,并使用了歧视性分析进行预测.
主要成果:
- 获得了高的确定系数 (R2 = 0.98).
- 显示了23秒的低平均预测误差.
- 在准确性和效率方面表现优于目前公布的保留时间预测模型.
结论:
- 开发的模型为保留时间预测提供了通用和高吞吐量解决方案.
- 为快速预测未知的污染物和数据挖掘提供科学基础.
- 在分析化学中显著推进非目标选应用.
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