Twelve 2C-X, six DOX, and fourteen 25X-NBOMe hallucinogenic phenethylamines, including two deuterated derivatives, were analyzed using UPLC-QTOF-MS with collision-induced dissociation at 10, 20, and 40 eV. Common neutral and radical losses (e.g., NH3, •CH6N, C2H7N, C2H9N) and characteristic product ions were identified for each class. Novel analogues can be detected by applying neutral loss filters and extracting these common product ions, enabling detection of rapidly changing new psychoactive substances without targeted screening.
New psychoactive substances (NPS) mimic existing drugs and can be highly potent, leading to frequent intoxications. A key target for hallucinogenic NPS is the 5-HT2A receptor. Measuring receptor binding affinity (Ki) through in vitro assays is resource-intensive. Using publicly available Ki data for 5-HT2A, five classification machine learning models were trained with molecular descriptors and fingerprints. The models achieved precisions and recalls up to 93% and 92%, respectively. Explainable artificial intelligence (SHAP values and similarity maps) was used for interpretation. The results align with previous experiments and support the models' suitability for predicting binding affinities of potential 5-HT2A ligands.