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IgFold 1.0.0 - #76
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IgFold 1.0.0#76
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- Support current PyTorch (>= 2.0, safe loading) and transformers (>= 4.36 including 5.x); drop the PyTorch-Lightning dependency; model is a plain nn.Module - Weights in safetensors format with the architecture config in file metadata; converter for legacy .ckpt files; IGFOLD_WEIGHTS_DIR override; AntiBERTy weights come from the Hugging Face Hub - Structures written with Biopython (PDB or mmCIF by extension); per-chain residue numbering - Input validation with clear errors; optional dependencies raise MissingDependencyError - Templates aligned to input sequences (mutations and missing residues tolerated) - Renumbering and Fv truncation with ANARCII (pip-installable) instead of AbNumber - Batched prediction (fold_batch/predict), pooled sequence embeddings, explicit device selection - igfold command-line interface (fold, convert-weights) - Fixes: chain-boundary masks in refinement losses, coordinates from the final refined frames, terminal O placement, OpenMM >= 8 units, padded-batch attention masking - pixi environments (PyPI dependencies, conda for Python only), tests with golden structures, pre-commit, CI on Linux and macOS, PyPI trusted-publishing workflow; rewritten Colab notebook Co-Authored-By: Claude Fable 5.1 <[email protected]>
Co-Authored-By: Claude Fable 5.1 <[email protected]>
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Modernization release. Summary:
igfoldCLI🤖 Generated with Claude Code