From Mass to Class: Classification of Amphetamines MS/MS Spectra via Graph Neural Networks

Identifying emerging novel psychoactive substances (NPS), using liquid chromatography high-resolution tandem mass spectrometry (LC-HR-MS/MS), is often hindered by their frequent absence from mass spectral reference libraries. We developed a graph neural network (GNN) classification model to detect amphetamine derivatives, a subclass of NPS, based on their fragmentation patterns. The data set was generated by transforming high-resolution mass spectra from mass/charge (m/z) values into the chemical compositions of the fragments, from which fully connected graphs were constructed. Spectra were extracted from the NIST23 database, using only protonated HCD spectra to ensure consistency, and collision energy was included as a parameter to enhance spectral informativeness. The network architecture and hyperparameters were systematically optimized, including network layout and pooling layer type. To address the class imbalance in the data set, where amphetamine derivatives were outnumbered 100:1 by nonamphetamines, the loss function was adjusted to penalize false negatives 50 times more than false positives. Evaluation of model performance on a randomly selected 10% test subset yielded a recall of 0.86 and a precision of 0.11. The model successfully detected the spiked amphetamine derivative in a human plasma sample, achieving a 1% false positive rate and demonstrating its applicability to complex biological matrices.