Assessing Modern AI-Driven Protein-Ligand Modeling with Phenethylamine and Tryptamine Psychedelics
Benjamin Cummins, Charles D. Nichols
AI Chemistry February 10, 2026 Peer reviewed DOI: 10.3390/aichem1010004 via OpenAlex
Summary
AI-based protein–ligand cofolding methods show promise in predicting binding orientations for psychedelics at the serotonin 5HT2A receptor, often aligning more closely with experimental structures compared to classical docking methods. However, classical docking still outperforms AI-driven approaches on average. The study highlights significant variability in performance among different modeling strategies and stresses the importance of experimental validation in drug discovery efforts involving serotonergic compounds.
Study at a glance
| Design | comparative study |
|---|---|
| Population | psychedelics bound to the serotonin 5HT2A receptor |
| Key finding | AI-based cofolding methods often produce binding orientations that closely resemble experimental structures, while classical docking shows greater variability but performs better on average. |
Abstract
Modern advances in artificial intelligence have accelerated the development of computational tools for protein–ligand structure prediction, yet their real-world performance remains uneven across receptor classes and ligand chemotypes. Recently published cryo-EM structures of several different psychedelics bound to the serotonin 5HT2A receptor provide a unique opportunity to explore how modern AI-based modeling performs in a pharmacologically important GPCR system. Here, we compare three major approaches: AI-based protein–ligand cofolding (Boltz-2), a leading AI-driven docking module (Uni-Mol Docking v2), and a widely used classical physics-based docking pipeline (AutoDock Vina) across a series of tryptamine and phenethylamine psychedelics. Predicted binding poses were comparatively assessed through structural alignment with these newly available cryo-EM complexes. Additionally, calcium-mobilization assays were performed to provide a coarse functional readout for comparison with computationally predicted binding affinities. This study integrates methodological review with exploratory benchmarking to illustrate how different modeling paradigms behave on a shared receptor–ligand test set. Our results highlight substantial variation between modeling strategies, with AI-based cofolding often producing global binding orientations more closely resembling experimental structures, and classical docking showing greater variability across ligands, while still outperforming AI-driven docking on average. These observations underscore both the growing utility and current limitations of AI-assisted structure prediction in serotonergic drug discovery, and emphasize the importance of careful, experimentally anchored evaluation as such tools continue to advance.