Predicting the retention time of Synthetic Cannabinoids using a combinatorial QSAR approach
Lina Wu,
Fu Xiao,
Xiaomin Luo,
Keming Yun,
Di Wen,
Jiaman Lin,
Shuo Yang,
Tianle Li,
Ping Xiang,
Yan Shi
Affiliations
Lina Wu
Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai 200063, PR China; Shanxi Medical University, Jinzhong 030600, PR China
Fu Xiao
School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, PR China; Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Science, 555 Zuchongzhi Road, Shanghai 201203, PR China
Xiaomin Luo
School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, PR China; Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Science, 555 Zuchongzhi Road, Shanghai 201203, PR China
Keming Yun
Shanxi Medical University, Jinzhong 030600, PR China
Di Wen
Hebei Medical University, Shijiazhuang 050017, PR China
Jiaman Lin
Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai 200063, PR China; Shanxi Medical University, Jinzhong 030600, PR China
Shuo Yang
Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai 200063, PR China
Tianle Li
Shanxi Medical University, Jinzhong 030600, PR China
Ping Xiang
Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai 200063, PR China; Corresponding author. Academy of Forensic science, Shanghai Key Laboratory of Forensic Medicine, No. 1347 Guangfuxi Road, Shanghai 200063, PR China.
Yan Shi
Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai 200063, PR China; Corresponding author. Academy of Forensic science, Shanghai Key Laboratory of Forensic Medicine, No. 1347 Guangfuxi Road, Shanghai 200063, PR China.
Background: Abuse of Synthetic Cannabinoids (SCs) has become a serious threat to public health. Due to the various structural and chemical group modified by criminals, their detection is a major challenge in forensic toxicological identification. Therefore, rapid and efficient identification of SCs is important for forensic toxicology and drug bans. The prediction of an analyte's retention time in liquid chromatography is an important index for the qualitative analysis of compounds and can provide informatics solutions for the interpretation of chromatographic data. Methods: In this study, experimental data from high-resolution mass spectrometry (HRMS) are used to construct a regression model for predicting the retention time of SCs using machine learning methods. The prediction ability of the model is improved by adopting a strategy that combines different descriptors in different independent machine-learning methods. Results: The best model was obtained with a method that combined Substructure Fingerprint Count and Finger printer features and the support vector regression (SVR) method, as it exhibited an R2 value of 0.81 for the validation set and 0.83 for the test set. In addition, 4 new SCs were predicted by the optimized model, with a prediction error within 3%. Conclusions: Our study provides a model that can predict the retention time of compounds and it can be used as a filter to reduce false-positive candidates when used in combination with LC-HRMS, especially in the absence of reference standards. This can improve the confidence of identification in non-targeted analysis and the reliability of identifying unknown substances.
