BoltzGen is a deep learning pipeline for computational protein and peptide binder design. It provides a command-line interface (boltzgen) that orchestrates a multi-step workflow: diffusion-based backbone generation, inverse folding (sequence design given structure), folding with Boltz-2, affinity prediction, analysis, and filtering/ranking of designs. The system supports diverse protocols including protein, peptide, nanobody, antibody, and small-molecule targeting, with extensive conditioning options like binding sites, disulfide bonds, cyclization, and per-residue constraints. The codebase is structured as a Python package with modular components for data parsing (YAML design specs, mmCIF/PDB), featurization, tokenization, cropping, and model layers (pairformer, attention, diffusion, inverse folding). It includes training pipelines with Hydra configuration, PyTorch Lightning, and validation splits. While primarily an application, it also contains research-grade example datasets and a Jupyter notebook for filtering, reflecting its origin in a bioRxiv publication. The architecture is well-organized but shows some technical drift (hardcoded paths, print statements, broad exception handling) and dependency on specific CUDA versions.
IgGM is a generative foundation model for antibody and nanobody design, implemented as a deep learning library with a command-line interface. The system performs tasks such as CDR loop design, framework region redesign (humanization), inverse design from complex structures, affinity maturation, and structure prediction. It uses a diffusion-based approach to co-design amino acid sequences and 3D backbone structures, leveraging an ESM2-based protein language model, Evoformer blocks with triangular attention, invariant point attention, and a structure module similar to AlphaFold2. The pipeline is orchestrated by the `AbDesigner` class and invoked via the `design.py` script, accepting PDB and FASTA inputs and generating output sequences and structures. The repository includes well-structured reusable components for rigid-body transformations (Rotation, Rigid), PDB parsing, coordinate conversion, and diffusion schedules, though some research artifacts (misspellings, commented code) and dependency pinning to older PyTorch/CUDA versions indicate moderate technical drift.