Discovery and mechanism analysis of novel TRPV1 antagonists driven by multi-dimensional virtual screening and dynamic simulation.

The transient receptor potential vanilloid type 1 (TRPV1) channel, a non-selective cation channel, is widely implicated in diverse physiological and pathological processes, including nociception and inflammation. Its modulators exert analgesic effects with a low risk of addiction, rendering TRPV1 a highly promising target for drug development. However, existing TRPV1 modulators are frequently hindered by dose-limiting side effects, such as dysregulation of core body temperature. To identify high-potency TRPV1 modulators with novel chemical scaffolds, this study developed an integrated computational pipeline that combines multi-dimensional virtual screening strategies: machine learning-based classification models, quantitative structure-activity relationship analysis, and three-dimensional shape similarity screening. Using this pipeline, a panel of candidate molecules was selected from a large-scale compound library and further validated via cellular activity assays and molecular dynamics simulations. Among these candidates, compounds C129-0055, Z617487438, and STK436742 exhibited robust TRPV1 inhibitory activity, with IC50 of 0.95 μM, 2.80 μM, and 0.69 μM, respectively. Site-directed mutagenesis experiments confirmed stable interactions between compounds C129-0055/STK436742 and the S512 residue of TRPV1. This study not only validates the utility of the integrated virtual screening strategy for discovering novel TRPV1 modulators but also provides promising lead scaffolds for the development of non-addictive analgesic agents.