Integrative Computational Strategies for Dynamic GPCR Landscapes: From Conformational Mechanisms to Drug Design

ABSTRACT

G protein‐coupled receptors (GPCRs) are the largest superfamily of membrane proteins and remain one of the most important classes of therapeutic targets. Although structural biology has provided valuable static structures for drug discovery, the translation of these insights into effective therapeutic strategies remains challenging because GPCR function and regulation are governed by complex conformational dynamics, metastable‐state transitions, and long‐range allosteric coupling. This review examines how computational strategies, including molecular dynamics (MD) simulations, artificial intelligence (AI) methods, and their integration, are being used to bridge this gap across three major areas, namely structural landscape mapping, mechanistic elucidation, and drug discovery. First, computational approaches for defining receptor architecture are summarized, combining static structure prediction with dynamic refinement to map conformational ensembles. Then, binding‐site discovery and dynamic characterization are discussed, along with the identification of metastable states and the reconstruction of free energy landscapes. Subsequently, the use of AI and MD in elucidating signaling mechanisms is examined. Particular attention is given to decoding ligand binding and dissociation, allosteric communication networks, and the structural basis of biased signaling and effector coupling. Finally, computational workflows for therapeutic development, including virtual screening and the optimization of lead compounds, are highlighted. Future directions for integrating AI and MD are discussed, with the goal of moving GPCR research from static structural description toward dynamic mechanistic understanding and next‐generation therapeutic development.