Explainable Artificial Intelligence (xAI) for 5-HT2A Receptor Binding Affinity of New Psychoactive Substances.
Molecules (Basel, Switzerland) June 1, 2026 Verena Schöning, Katharina Elisabeth Grafinger, Daniel Pasin et al.
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.