准确预测同热气体色谱科瓦茨保留指数的准确预测
Afia Anjum1, Jaanus Liigand2, Ralph Milford2
1Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E9, Canada.
Journal of chromatography. A
|July 6, 2023
概括
RIpred是一个新的网络服务器,可以准确预测各种化学品的气色谱Kováts保留指数 (RI). 该工具使用图形神经网络 (GNN) 为不同的静态相和化合物形式提供快速可靠的预测.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 化学信息学 化学信息学
背景情况:
- 准确预测气色谱Kováts保留指数 (RI) 对于化学物质的识别和分析至关重要.
- 现有的RI预测方法可能缺乏速度,准确性或适用于各种化学结构和静止相.
研究的目的:
- 开发并介绍RIpred,这是一个免费可用的网络服务器,用于快速准确地预测RI.
- 为广泛的衍生和未衍生GC可调节化合物提供跨常规静止相的预测.
主要方法:
- 使用一个图形神经网络 (GNN),在精选的气色谱科瓦茨保留指数数据上进行训练,这些数据来自NIST 17和NIST 20数据库.
- 输入特征包括从SMILES字符串和原子级特征中获得的分子图形.
- 模型的性能使用10倍交叉验证和对持有组的测试进行了评估.
主要成果:
- 在不同静止相 (SSNP,SNP,SP) 中,RIpred 实现了高精度,平均绝对误差 (MAE) 低于 73 RI 单位.
- 大多数预测的平均绝对百分比误差 (MAPE) 通常在3%以内.
- RIpred表现出与现有的最先进的衍生化合物模型相提并论的性能.
结论:
- RIpred提供了一个快速,准确和易于使用的工具来预测RI,支持化学分析和研究.
- 网络服务器为人类代谢数据库 (HMDB 5.0) 中的化合物提供了广泛的预测RI值.
- RIpred解决了对各种GC条件和化学类型的可靠RI预测的重大需求.
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