Peldom/papers_for_protein_design_using_DL

Curated collection of deep learning papers organized by design paradigm—including function-to-scaffold, scaffold-to-sequence, and function-to-sequence approaches—with structured coverage of diverse architectures (transformers, diffusion models, GANs, reinforcement learning) and application domains (antibodies, binders, enzymes, peptides). Integrates benchmark datasets, protein language models, and mutation effect prediction frameworks to support both de novo design and structure-guided sequence generation workflows.

1,898 stars. Actively maintained with 5 commits in the last 30 days.