Abstract：
In this study, a novel approach is introduced, merging in silico prediction with a Convolutional Neural Network (CNN) framework for the targeted screening of in vivo metabolites in Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) fingerprints. Initially, three predictive tools, supplemented by literature, identify potential metabolites for target prototypes derived from Traditional Chinese Medicines (TCMs) or functional foods. Subsequently, a CNN is developed to minimize false positives from CWT-based peak detection. The Extracted Ion Chromatogram (EIC) peaks are then annotated using MS-FINDER across three levels of confidence. This methodology focuses on analyzing the metabolic fingerprints of rats administered with \"Pericarpium Citri Reticulatae - Fructus Aurantii\" (PCR-FA). Consequently, 384 peaks in positive mode and 282 in negative mode were identified as true peaks of probable metabolites. By contrasting these with \"blank serum\" data, EIC peaks of adequate intensity were chosen for MS/MS fragment analysis. Ultimately, 14 prototypes (including flavonoids and lactones) and 40 metabolites were precisely linked to their corresponding EIC peaks, thereby providing deeper insight into the pharmacological mechanism. This innovative strategy markedly enhances the chemical coverage in the targeted screening of LC-HRMS metabolic fingerprints.
摘要：
在这项研究中,引入了一种新颖的方法，将计算机预测与卷积神经网络（CNN）框架合并，以在液相色谱-高分辨率质谱（LC-HRMS）指纹图谱中靶向筛选体内代谢物。最初，三个预测工具，辅以文学，鉴定来自中药（TCM）或功能性食品的目标原型的潜在代谢物。随后，CNN被开发以最小化来自基于CWT的峰值检测的误报。然后使用MS-FINDER在三个置信水平上注释提取的离子色谱(EIC)峰。该方法的重点是分析“陈皮-耳”（PCR-FA）给药大鼠的代谢指纹。因此,阳性模式下的384个峰和阴性模式下的282个峰被鉴定为可能代谢物的真实峰。通过将这些与“空白血清”数据进行对比，选择适当强度的EIC峰用于MS/MS片段分析。最终,14个原型（包括类黄酮和内酯）和40个代谢物精确地连接到它们相应的EIC峰，从而提供更深入的药理学机制。这种创新策略显着增强了LC-HRMS代谢指纹的靶向筛选中的化学覆盖率。
