Diffusion-Based Protein Structure Design: Geometric Modelling, Validation Strategies, and Thermodynamic Challenges.

Although deep learning has transformed protein structure prediction, the controlled generation of functional and experimentally tractable protein structures remains a major challenge in structural bioinformatics. Diffusion models offer a versatile approach to generating protein backbones, motif-conditioned scaffolds, all-atom structures and biomolecular interaction geometries, while accommodating explicit structural and functional constraints. This review focuses on coordinate- and residue-frame-based diffusion approaches for generating protein structures, paying particular attention to geometric equivariance, conditioning strategies, all-atom modelling and interaction-aware design. We compare representative methods derived from RoseTTAFold, frame-diffusion architectures, and oriented-residue-cloud representations according to their molecular representation, generative objective, and validation strategy. We examine the criteria used to evaluate generated proteins, such as stereochemical quality, structural consistency, designability, novelty, diversity, computational efficiency, and experimental performance. Particular attention is given to the distinction between learned structural distributions and condition-dependent thermodynamic ensembles. Future progress will depend on the integration of generative models with molecular mechanics, conformational sampling, uncertainty estimation, free-energy methods, and experimental design-build-test-learn cycles. Within this framework, diffusion models offer candidate generation and constraint satisfaction capabilities within broader protein engineering workflows.