Screening Unknown Novel Psychoactive Substances Using EI GC-MS Based Machine Learning

In recent years, there is a large increase in structural diversity of novel psychoactive substances (NPS), exacerbating drug abuse issues as these variants evade classical detection methods by spectral library matching. Electron ionization (EI) gas chromatography mass spectrometers (GC-MS) are commonly used to identify controlled substances, in particular NPS. To tackle this issue, machine learning models are constructed to address the analytical challenge of identifying unknown NPS, using only GC-MS data. 891GC-MS spectra are used to train and evaluate multiple supervised machine learning classifiers, namely artificial neural network (ANN), convolutional neural network (CNN) and balanced random forest (BRF). 7 classes, comprising 6 NPS classes (cathinone, cannabinoids, phenethylamine, piperazine, tryptamines and fentanyl) and other unrelated compounds can be effectively classified with an average F1 score greater than 0.9, averaged across cross-validation folds. These results indicate that machine learning models are a promising complement as an effective NPS detection tool.