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

ICML 2025 Workshop FM4LS Submission47 Authors

Keywords: Generative Protein Design, Score Matching, Flow Matching, Structural Phylogenetics, Molecular Dynamics, Ligand Docking

Abstract:

Deep generative models show promise for $\textit{de novo}$ 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 $\textit{de novo}$ 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.

Submission Number: 47