Physics-Informed Generative Modeling for De Novo Protein Design Under Thermodynamic and Structural Constraints

Abstract

De novo protein design has entered a new phase in which generative machine learning systems propose sequences and structures that are not directly observed in nature. These systems promise advances in therapeutics, biocatalysis, materials, and environmental sensing, but their outputs must satisfy thermodynamic stability, structural compatibility, functional specificity, and manufacturability constraints. This paper examines physics-informed generative modeling as a socio-technical infrastructure rather than a narrow algorithmic pursuit. It argues that thermodynamic and structural constraints should be embedded across data generation, model architecture, training objectives, evaluation pipelines, and deployment governance. The analysis considers trade-offs among all-atom fidelity, computational tractability, equivariance, robustness, interpretability, and equity of access. It also evaluates infrastructure requirements, including high-performance computing, curated structural databases, reproducible evaluation suites, model documentation, and sustainability accounting. Cross-domain comparisons with molecular design, weather modeling, and safety-critical machine learning clarify how failure modes propagate when physical plausibility is treated as an afterthought. The paper concludes that successful de novo protein design requires hybrid architectures that couple generative proposal mechanisms with physics-based validation, uncertainty quantification, and governance mechanisms that make limitations visible to downstream users. Without such integration, generative models may produce aesthetically plausible but thermodynamically fragile or functionally irrelevant designs. With it, physics-informed generative systems can expand the space of viable proteins while maintaining scientific accountability, operational robustness, and public trust.