A Novel Nontargeted Screening Strategy for New Psychoactive Substances: A Case Study of Synthetic Cannabinoids Based...

Nontargeted screening of new psychoactive substances (NPSs) has always been a challenging task, typically involving data acquisition, the extraction of suspicious peaks, and mass spectrometry elucidation in the screening process. The ongoing advancement of instrument acquisition technology and data analysis methods has resulted in an increasing amount of sample data requiring manual elucidation, significantly reducing the efficiency of forensic identification work and leading to issues such as missed detections and false positives. This study proposed a novel nontargeted screening strategy that is capable of automatically elucidating the NPS classes and chemical structures of unknown designer drugs. For practical use, we applied electron-activated dissociation (EAD) technology to analyze 181 synthetic cannabinoids (SCs) and developed novel mass spectrometry intelligent elucidation (MSIE) software to achieve the nontargeted screening of NPSs and automated structural elucidation of SCs. MSIE software comprises an NPS nontargeted screening model, an SC subclass classification model, and a mass spectrometry intelligent elucidation algorithm. The NPS nontargeted screening model was trained on CID data from 505 NPSs, achieving the classification of 8 NPS classes, with the highest F1 score reaching 93.3%. The SC subclass classification model was trained on EAD data from 181 SCs, achieving the classification of 7 SC parent structures, with the highest F1 score reaching 95.3%. The mass spectrometry intelligent elucidation algorithm includes functionalities such as candidate chemical structure generation, spectral prediction, candidate structure scoring, and fragment ion peak matching, all without any manual intervention throughout the entire process.