Serotonin 5-HT2A receptors are found throughout the body and are most dense in brain cortical layer V. They are involved in normal physiology and neuropsychiatric diseases like schizophrenia. Atypical antipsychotics block these receptors, while psychedelic drugs such as psilocybin, dimethyltryptamine, and lysergic acid diethylamide activate them to produce lasting therapeutic effects in clinical trials for major depression and substance use disorders. The three main agonist scaffolds—tryptamines, ergolines, and phenylalkylamines—engage different amino acid residues in the receptor binding pocket, leading to functionally selective outcomes. Understanding these ligand-receptor interactions guides future drug discovery for optimized therapeutics.
Modern AI-based tools for predicting how drug-like molecules bind to proteins show uneven performance across different receptor types and chemical classes. Newly available cryo-electron microscopy structures of several psychedelic compounds bound to the serotonin 5HT2A receptor, an important G protein-coupled receptor, allowed comparison of three modeling approaches: AI-based protein–ligand cofolding (Boltz-2), an AI-driven docking module (Uni-Mol Docking v2), and a classical physics-based docking pipeline (AutoDock Vina). Predicted binding poses were compared with the experimental structures, and calcium-mobilization assays provided a functional readout. AI-based cofolding often produced global binding orientations closer to experimental structures, while classical docking showed greater variability across ligands but outperformed AI-driven docking on average. The findings highlight both the growing utility and current limitations of AI-assisted structure prediction in serotonergic drug discovery.