AI-SNPS2:一个多层LC-MS/MS平台,集成分子网络和保留时间预测,用于识别受控和新型精神活性物质
So Yeon Lee1, Jihyun Lee2, Jaewoo Song2
1Department of Chemistry, Sogang University, Baekbeom-Ro 35, Mapo-Gu, Seoul 04107, Republic of Korea.
Analytical chemistry
|October 27, 2025
概括
法医科学家现在可以使用AI-SNPS2软件识别新型合成药物. 该工具通过分析光谱数据和预测保留时间来增强未知物质的检测,改进了传统方法.
科学领域:
- 法医科学 法医科学 法医科学
- 分析化学 分析化学
- 计算化学的计算化学
背景情况:
- 新出现的未知受控物质对法医和分析科学构成重大挑战.
- 传统的液体染色学-双重质谱法 (LC-MS/MS) 在识别现有光谱库中不存在的新型合成类似物方面受到限制.
研究的目的:
- 开发一个增强的选软件,AI-SNPS2 (麻醉药物和新精神活性物质的人工智能选器2),以解决识别未知受控物质的局限性.
- 通过集成通过光谱相似性和色谱可信性过的结构模拟检测来扩展识别能力,超越传统的光谱搜索.
主要方法:
- AI-SNPS2集成了五个层:LC-MS查看器,AI分类器,识别器,NetBuilder (分子网络) 和RT预测器 (使用机器学习预测保留时间).
- 该RT预测器层使用了四种回归模型 (ANN,SVR,RF,XGBoost) 训练了164种受控物质的分子描述符.
- 通过将已知的合成大麻素 (JWH-019,JWH-015,JWH-302) 添加到复杂矩阵中并分析额外的化合物来评估软件实用程序.
主要成果:
- RT预测模型表现出强的性能,XGBoost获得了最高的准确性 (R2 = 0.964,MAE = 0.585).
- 在110分钟的梯度上,RT预测偏差在几分钟内出现,这证明了它对候选过的实用性.
- AI-SNPS2成功地使用分子网络和混合相似性搜索识别了尖合成大麻素,并且在检测数据库中缺少的化合物方面表现出了希望.
结论:
- AI-SNPS2通过结合光谱相似性,分子网络和保留时间预测,提供了一种先进的方法来识别新型精神活性物质和受控类型.
- 该软件显著提高了法医和分析实验室在检测和识别未知化合物的能力,提高了公共安全.
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