厦大Nature：首个反向脂质组学AI模型，分析速度飙升6万倍

Core Viewpoint - The article discusses the introduction of LipidIN, a novel AI model for lipidomics that significantly enhances the speed and depth of lipid identification while enabling platform-independent analysis [1][3]. Group 1: Importance of Lipids - Lipids are crucial molecules in living organisms, serving as structural components of cell membranes, energy reserves, and signaling messengers [2]. - Lipidomics can reveal metabolic abnormalities linked to major diseases such as cardiovascular diseases, tumors, and diabetes, but the complexity of lipid molecules presents significant research challenges [2]. Group 2: Development of LipidIN - The research team from Xiamen University published their findings in Nature Communications, introducing LipidIN as the first rapid, platform-independent reverse lipidomics mass spectrometry AI model [3]. - LipidIN integrates the lipid categories intelligence (LCI) model and the wide-spectrum modeling yield network (WMYn) model for lipid mass spectrometry data analysis [4]. Group 3: Features of LipidIN - LipidIN employs a four-tier workflow: 1. Establishing a tiered spectral library covering 121 lipid classes with 168 million theoretical spectra 2. Implementing a rapid search module capable of over 100 billion spectral matches per second 3. Utilizing the LCI model to reduce false positive rates 4. Generating high-resolution secondary fingerprint spectra through WMYn [5][6]. Group 4: Performance Evaluation - The platform demonstrated stable performance across various biological samples and successfully adapted to different technical platforms, providing significant technical assurance for data comparability in the field [6]. - LipidIN achieved a false discovery rate of 5.7% and successfully annotated 8,923 lipid species across species, with a 20% improvement in target molecule recall rate [6]. Group 5: Comparative Analysis - In performance comparisons with existing tools like MS-DIAL, LipidSearch, and others, LipidIN showed a recall rate of over 90% when using its tiered spectral library strategy, significantly outperforming traditional methods [12]. - The LCI module of LipidIN outperformed the LDA method in annotation accuracy across most lipid subclasses, demonstrating its effectiveness in removing false positives [13]. Group 6: High-Resolution Spectrum Reconstruction - The WMYn model was systematically compared with various spectrum prediction methods, showing superior prediction accuracy, especially at high resolution [14]. - The integration of a five-tier spectral library into the Entropy Search framework further enhanced the recall rates for various lipid subclasses, validating LipidIN's unique value in complex lipid analysis [14].