Explainable Artificial Intelligence (xAI) for 5-HT2A Receptor Binding Affinity of New Psychoactive Substances.

Abstract

New psychoactive substances (NPS) are a heterogeneous group of recreational drugs that mimic the actions and psychoactive effects of existing pharmaceutical products or recreational drugs. As NPS can be highly potent, even exceeding their template compound's potency, there are frequent reports of non-fatal and fatal intoxications. One principal target for hallucinogenic and psychedelic drugs, including NPS, is the 5-hydroxytryptamine receptor 2A (5-HT2A). Since NPS are designed to evade legal restrictions, this drug market is quickly evolving, and researchers are playing catch-up, investigating these novel compounds for their toxicological and pharmacological properties. Receptor binding affinity (Ki) is an important property describing ligand-receptor interactions and is a prerequisite for receptor activation. Competitive in vitro assays can be used to assess Ki; this is a resource-intensive process. We used publicly available Ki data for the 5-HT2A, calculated molecular descriptors and fingerprints, and trained five classification machine learning models. The predictive performance of the models had precisions and recalls up to 93% and 92%, respectively. We used explainable artificial intelligence, i.e., SHAP values and similarity maps, for model interpretation. The results are in line with previous experiments and support its suitability to predict the binding affinities of possible 5-HT2A ligands.