Interpretable Machine Learning Model of Receptor Dynamics Reveals AT1R Allostery and a Negative Allosteric Modulator
This work developed an interpretable probabilistic framework that extracts residue-level dependencies from MD ensembles using Bayesian network modeling (BNM) and establishes a generalizable and interpretable framework for uncovering GPCR allosteric communication networks and discovering modulators that exploit these ne...