Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards

ArXi:2603.26378v1 Announce Type: cross Generative modeling has become a central paradigm in protein research, extending machine learning beyond structure prediction toward sequence design, backbone generation, inverse folding, and biomolecular interaction modeling. However, the literature remains fragmented across representations, model classes, and task formulations, making it difficult to compare methods or identify appropriate evaluation standards.