Predicting the retention time of Synthetic Cannabinoids using a combinatorial QSAR approach
文献类型：期刊论文
| 作者 | Wu, Lina1,2; Xiao, Fu3,4; Luo, Xiaomin3,4 ; Yun, Keming2; Wen, Di5; Lin, Jiaman1,2; Yang, Shuo1; Li, Tianle2; Xiang, Ping1,6; Shi, Yan1,6
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| 刊名 | HELIYON
 |
| 出版日期 | 2023-06-01 |
| 卷号 | 9期号:6页码:9 |
| 关键词 | Synthetic cannabinoids Retention time Machine learning QSAR |
| DOI | 10.1016/j.heliyon.2023.e16671 |
| 通讯作者 | Xiang, Ping(xiangping2630@163.com) ; Shi, Yan(shiy@ssfjd.cn) |
| 英文摘要 | 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. |
| WOS关键词 | IN-SILICO PREDICTION ; SUPPORT |
| 资助项目 | National Key Research and Development Program of China[2022YFC3302003] ; National Natural Science Foundation of China[82271932] ; Central Guidance on Local Science and Technology Development Fund of Hebei Province[21DZ2270800] ; Shanghai Key Laboratory of Forensic Medicine[19DZ2292700] ; Shanghai Forensic Service Platform[81971789] ; [226Z5601G] |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| 出版者 | CELL PRESS |
| WOS记录号 | WOS:001043477100001 |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/306899]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Xiang, Ping; Shi, Yan |
| 作者单位 | 1.Acad Forens Sci, Shanghai Key Lab Forens Med, Shanghai 200063, Peoples R China 2.Shanxi Med Univ, Jinzhong 030600, Peoples R China 3.Nanjing Univ Chinese Med, Sch Chinese Mat Med, Nanjing 210023, Peoples R China 4.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China 5.Hebei Med Univ, Shijiazhuang 050017, Peoples R China 6.Acad Forens Sci, Shanghai Key Lab Forens Med, 1347 Guangfuxi Rd, Shanghai 200063, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Lina,Xiao, Fu,Luo, Xiaomin,et al. Predicting the retention time of Synthetic Cannabinoids using a combinatorial QSAR approach[J]. HELIYON,2023,9(6):9. |
| APA | Wu, Lina.,Xiao, Fu.,Luo, Xiaomin.,Yun, Keming.,Wen, Di.,...&Shi, Yan.(2023).Predicting the retention time of Synthetic Cannabinoids using a combinatorial QSAR approach.HELIYON,9(6),9. |
| MLA | Wu, Lina,et al."Predicting the retention time of Synthetic Cannabinoids using a combinatorial QSAR approach".HELIYON 9.6(2023):9. |
入库方式： OAI收割
来源：上海药物研究所
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