使用组合式QSAR方法预测合成大麻素的保留时间
Lina Wu1,2, Fu Xiao3,4, Xiaomin Luo3,4
1Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai 200063, PR China.
法医毒理学家现在可以使用新的机器学习模型预测合成大麻素 (SC) 保留时间. 这个工具有助于识别未知的药物,改善公共卫生和禁毒执法.
科学领域:
- 法医毒理学 法医毒理学
- 计算化学计算化学
- 数据科学数据科学数据科学
背景情况:
- 滥用合成大麻素 (SC) 构成严重的公共健康风险.
- 由于非法制造商经常进行结构修改,检测SCs具有挑战性.
- 准确的保留时间预测对于法医毒理学的定性分析至关重要.
研究的目的:
- 开发一种机器学习模型,使用高分辨率质谱 (HRMS) 数据预测SC保留时间.
- 通过结合多种分子描述符和机器学习算法来提高预测准确性.
主要方法:
- 利用实验HRMS数据来训练回归模型.
- 采用了一种战略,将子结构指纹计数和指纹功能与支向量回归 (SVR) 结合起来.
- 使用独立验证和测试集验证模型性能.
主要成果:
- 实现了高预测准确度,R值为0.81 (验证) 和0.83 (测试集).
- 优化的模型成功地预测了4个新的SC,错误率低于3%.
- 证明了该模型在改进色谱数据解释方面的有效性.
结论:
- 开发了一种可靠的模型来预测化合物保留时间,帮助法医识别.
- 该模型作为一个有价值的过器,以减少LC-HRMS分析中的假阳性,特别是没有参考标准.
- 提高了对识别未知的物质的信心,并支持法医毒理学中的非向分析.
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