ABSTRACT Small‐angle X‐ray and neutron scattering (SAXS/SANS), combined with molecular dynamics (MD) simulations, have matured into a quantitative framework for refining protein structural ensembles, supported by methodologies spanning ensemble selection, maximum‐entropy reweighting, Bayesian inference, and replica‐averaged restrained simulations. Their extension to lipid membranes has lagged considerably. In this Perspective, we argue that this lag is conceptual rather than algorithmic: protein scattering reports on finite particles with a rich structural information content, whereas membrane scattering probes a quasi‐two‐dimensional fluctuating interface governed by collective elastic modes, with only a handful of independent structural channels accessible from the one‐dimensional bilayer form factor. We review the inference frameworks underpinning protein SAS–MD integration, examine the theoretical and experimental foundations of membrane scattering, and analyze model‐based and Hamiltonian‐level restrained‐ensemble approaches for bilayers. Six methodological directions are identified—spanning forward modeling, uncertainty quantification, multi‐observable integration, collective‐mode sampling, field‐based inference, and machine‐learning surrogates—that together define a tractable agenda for placing membrane ensemble refinement on the same statistical footing now established for proteins.
ABSTRACT The dynamic interactions between enzymes and substrates, as well as the accurate elucidation of enzymatic catalytic mechanisms, are key scientific questions in modern enzymology. The development of quantum chemical calculation methods provides a new technical means for the study of enzymatic catalytic mechanisms. Among these, the theozyme model can enormously simplify the required computations and has become an important method for exploring enzymatic catalysis. However, as catalytic reactions discovered grow increasingly complex, the theozyme model built from static structures is increasingly found to be inadequate for accurate explanations of catalytic mechanisms. We have now coupled molecular dynamics (MD) simulations with the theozyme model to create a dynamic theozyme model for the study of complex catalytic systems. The new strategy utilizes MD simulations of transition states or key intermediates to obtain structural information during catalysis, providing reliable guidance for constructing the theozyme model. We have applied this strategy to several distinct catalytic systems, successfully revealing catalytic mechanisms leading to regioselectivity and stereoselectivity. The strategy proposed herein effectively integrates dynamic interaction analysis with quantitative mechanism evaluation, providing a research paradigm that balances accuracy and efficiency for elucidating enzymatic catalytic mechanisms. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Structure and Mechanism > Reaction Mechanisms and Catalysis
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.
ABSTRACT Equivariant graph neural networks (EGNNs) are becoming the geometric infrastructure of 3D molecular generation for AI‐aided drug discovery. By enforcing E(n), E(3), or SE(3) equivariance, they separate physical molecular structure from arbitrary coordinate‐frame conventions and ensure that predicted coordinates, denoising directions, and velocity fields transform consistently with molecular geometry. This symmetry‐aware design supports direct modeling of conformations and protein–ligand spatial relationships while remaining compatible with diffusion, flow‐based, autoregressive, and hybrid generative frameworks. Consequently, EGNNs enable more geometrically consistent generation and facilitate controllable design conditioned on binding pockets, pharmacophores, fragments, scaffolds, reference ligands, or molecular properties. Their benefits, however, are bounded by what equivariance encodes. Coordinate‐frame consistency does not itself enforce valid bonds, valence, stereochemistry, topology–geometry agreement, synthesizability, or biological activity. Moreover, practical models must reconcile discrete chemical variables with continuous geometric dynamics, represent long‐range interactions without prohibitive computational cost, and account for protein flexibility, solvent, metal ions, and induced fit. Performance is further constrained by biased docking or property predictors, heterogeneous benchmarks, and limited prospective validation. This review synthesizes how EGNNs function across major generative paradigms and argues that future drug‐oriented systems should combine joint 2D–3D graph generation, physically credible interaction modeling, synthesis and ADMET constraints, multi‐objective optimization, and closed‐loop experimental feedback. EGNNs should therefore be viewed not as a complete chemical solution, but as the symmetry‐aware foundation upon which more testable and biologically relevant molecular design systems can be built.
ABSTRACT Spatial confinement radically changes the collective behavior of amphiphilic molecules, generating self‐assembled entities that significantly exceed bulk systems in morphological and topological complexity. Under geometric persuasion (and a modest nudge from entropy), these molecules orchestrate unexpected architectures: vesicle‐in‐vesicle nesting, fenestrated structures, and convoluted hydrophilic/hydrophobic chambers with curious properties for intracellular transport. Remarkably, confined systems echo the cell's spatially restricted nature, depicting fusion, fission, and engulfment events, suggesting that confinement does not merely impose boundary constraints but natural design principles: tune the cage, and the molecules will improvise a new choreography. Within nanosciences, recognizing confinement as a controllable parameter extends the traditional concept of self‐assembly from a passive chemical consequence into an engineerable route toward hierarchical 3D structures with programmable form and function, each one a little kingdom with its own rules for curvature and asymmetry. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Structure and Mechanism > Computational Materials Science Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods
