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Copy file name to clipboardExpand all lines: CHANGELOG.md
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### 0.6.0 - TBD
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- Added new baseline methods
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- Added new baseline methods (AlphaFold 3, NeuralPLexer3, Chai-1 with multiple sequence alignments (MSAs))
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- Added new binding site-focused implementation of `complex_alignment.py` based on PyMOL's `align` command, which in many cases yields 3x better docking evaluation scores for baseline methods
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- Added new script for analyzing baseline methods' protein conformational changes w.r.t. input (e.g., AlphaFold) protein structures and the corresponding reference (crystal) protein structures
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- Added the new centroid RMSD and PLIF-EMD/WM metrics
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- Added a failure mode analysis notebook
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- Introducing DockGen-E, a new version of the DockGen benchmark dataset featuring enhanced biomolecular context for docking and co-folding predictions - namely, now all DockGen complexes represent the first (biologically relevant) bioassembly of the corresponding PDB structure
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- For the single-ligand datasets (i.e., Astex Diverse, PoseBusters Benchmark, and DockGen), now providing each baseline method with primary *and cofactor* ligand SMILES strings for prediction, to enhance the biomolecular context of these methods' predicted structures - as a result, for these single-ligand datasets, now the predicted ligand *most similar* to the primary ligand (in terms of both Tanimoto and structural similarity) is selected for scoring
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- Updated Chai-1's inference code to commit `44375d5d4ea44c0b5b7204519e63f40b063e4a7c`
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- Updated Chai-1's inference code to commit `44375d5d4ea44c0b5b7204519e63f40b063e4a7c`, and ran it also with NeuralPLexer3's (paired) MSAs
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- Replaced all AlphaFold 3 server predictions of each dataset's protein structures with predictions from AlphaFold 3's local inference code
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- With all the above changed in place, simplified, re-ran, and re-analyzed all baseline methods for each benchmark dataset, and updated the baseline predictions hosted on Zenodo
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- Pocket-only benchmarking has been deprecated
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- With all the above changed in place, simplified, re-ran, and re-analyzed all baseline methods for each benchmark dataset, and updated the baseline predictions and datasets (now containing standardized MSAs) hosted on Zenodo
[](https://www.repostatus.org/#active)
**NOTE:** The preprocessed Astex Diverse, PoseBusters Benchmark, DockGen, and CASP15 data available via [Zenodo](https://doi.org/10.5281/zenodo.13858866) provide pre-holo-aligned protein structures predicted by AlphaFold 3 (and alternatively MIT-licensed ESMFold) for these respective datasets. Accordingly, users must ensure their usage of such predicted protein structures from AlphaFold 3 aligns with AlphaFold 3's [Terms of Use](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md).
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**NOTE:** The preprocessed Astex Diverse, PoseBusters Benchmark, DockGen, and CASP15 data available via [Zenodo](https://doi.org/10.5281/zenodo.14629652) provide pre-holo-aligned protein structures predicted by AlphaFold 3 (and alternatively MIT-licensed ESMFold) for these respective datasets. Accordingly, users must ensure their usage of such predicted protein structures from AlphaFold 3 aligns with AlphaFold 3's [Terms of Use](https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md).
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