google-deepmind/alphafold3 v3.0.2
google-deepmind/alphafold3
Captured source
source ↗AlphaFold v3.0.2
Repository: google-deepmind/alphafold3
Tag: v3.0.2
Published: 2026-04-20T12:46:34Z
Prerelease: no
Release notes: Version 3.0.2 provides new features, improves performance, improves documentation, and fixes bugs. AlphaFold v3.0.2 supports NVIDIA Blackwell GPUs out of the box. We thank everyone who reported issues, proposed new features, and submitted PRs!
Note that the AlphaFold 3 parameter files are compatible with any 3.0.x version. We bump the the major/minor version numbers only when a new model is released, otherwise only the patch version number is increased.
Change log
Below is a selection of new features, performance improvements and bug fixes. Full change log: v3.0.1...v3.0.2.
New features
- Better bond validation: residue upper bound, and validity of atom names within a residue are now checked.
- Better error messages if paths supplied instead of data in templates, MSA, or CCD.
- Allow supplying user-provided CCD using an external file.
- Added flag
--force_output_dirto force writing into an output directory even if it exists. - Added chirality checking utility.
- All output filenames are now unique, so flattening a directory of outputs is no longer an issue.
- Support for optionally saving the distogram using the
--save_distogramflag. Thanks @PasqM. - Added a flag to compress large output files (mmCIF and confidence JSON) using zstandard. Thanks @ntnn19.
Performance improvements
- Jackhmmer requires significantly less RAM when it finds excessive number of hits for a particular sequence.
- Support for searching against sharded databases in Jackhmmer and Nhmmer, enabling 10-30x MSA search when used. See https://github.com/google-deepmind/alphafold3/blob/main/docs/performance.md#sharded-genetic-databases for more details.
- Use Tokamax for the implementation of flash attention and SwiGLU. This also adds support for newer GPUs (e.g. NVIDIA Blackwell) and provides an option for further inference performance optimisations in the future. Big thanks to @rpachauri, @chr1sj0nes, @sbodenstein and other people working on Tokamax!
- Made saved embeddings 2x smaller by using
float16instead offloat32. - Significantly reduced the size of the Docker and Singularity containers by disabling pip cache.
- The Docker container build is faster thanks to using uv.
- Docker build should be slightly faster on machines with > 8 cores.
Bug fixes
- The output written in stdout is now cleaner and more consistent.
- Fixed token chain IDs in summary metrics to be the same as the input chain IDs.
- Fixed user CCD validation to check all provided entries.
- Fixed a crash in cases where Hmmsearch found no template hits (hapenning especially with short peptides).
- Added support for
useStructureTemplateandmaxTemplateDatein the AlphaFold Server JSON format. - Embeddings saved as part of the output are now correctly cropped instead of including padding up to the bucket size.
- User-provided unpaired MSA is no longer deduplicated and thus preserves the exact provided pairing. Thanks @MGordon09.
- Make pseudo_beta_fn work with both np and jnp. Thanks @popfido.
- All CCD entries should now parse without errors by correctly wiring through the max reference date. Thanks @jvermaas.
- Atom names are now validated in the bonds input list. Thanks @maxh190.
- Removed dependency on the
chexlibrary. - Output directory names now respect the casing in the folding job name. Thanks @adavasam.
- Fixed handling of CIF tokens that contain quotes surrounded by spaces in CifDict. Thanks @FilipSchymik.
- Reduced RAM use when there were multiple inputs provided with custom user CCD. Thanks @MichaelaBrezinova, @genius-0963.
- The pickle loader is now safe against maliciously crafted CCD pickles. Thanks @sanowl.
- Upgrade GitHub Actions to Node 24 compatible versions. Thanks @salmanmkc.
- The location of libcifpp (where AlphaFold 3 looks for the CCD) can be specified via the
LIBCIFPP_DATA_DIRenvironment variable. Thanks @cgross95, @Ashutosh0x. - Made sharded Jackhmmer result merging more robust in case of e-value ties
Documentation
- Documented the names, shapes, and dtypes of all AlphaFold 3 model parameters.
- The discrepancy in genetic search in AlphaFold 3 and AlphaFold Server is now documented in known issues. Thanks @stianale.
- Updated Docker build instructions for AlmaLinux/Rocky/RHEL users. Thanks @ocstx.
We thank the following people for their pull requests and/or great suggestions: @armanvnv, @Ashutosh0x, @ccoulombe, @genius-0963, @ishanrajsingh, @lucajovine, @MichaelaBrezinova, @noghte@ntnn19, @ojasshelke, @owainkenwayucl, @PasqM, @pavithra-rvce, @popfido, @salmanmkc, @sanowl, @zohaib-7035.
Notability
notability 6.0/10Minor update to top-tier protein model