Challenges and Guidelines in Deep Generative Protein Design: Four Case Studies

Deep generative models show promise for $\mathit{\text{de}}\mathit{\text{novo}}$ protein design, yet reliably producing designs that are geometrically plausible, evolutionarily consistent, functionally relevant, and dynamically stable remains challenging. We present a deep generative modeling pipeline for early $\mathit{\text{de}}\mathit{\text{novo}}$ design of monomeric proteins, based on Score Matching and Flow Matching. We apply this pipeline to four diverse protein families with an adaptable evaluation protocol. Generated structures display realistic, clash-free conformations enriched with family-specific features, while the designed sequences preserve essential functional residues while retaining variability. Molecular dynamics and binding simulations show dynamic stability, with wild-type-like binding pockets that interact favorably with family-specific ligands. These results provide practical guidelines for integrating generative models into protein design workflows.