AlphaProtein Novo Generator
Release in the AlphaProtein Novo family · version AP Novo Generator (generator.bin.zst)
Maintained by Google DeepMind13
The pretrained AP Novo Generator weights, a single generator.bin.zst file that the alphaprotein-novo package (JAX) loads to co-generate protein structures and amino acid sequences conditioned on a catalytic motif and ligand context. The weights are not in the repository; the README gives a direct download from Google Cloud Storage.165
- Repository: GitHub repository (alphaprotein-novo) (external site: github.com)
- License: AP Novo Generator Model Parameters Terms of Use (external site: github.com)
- Paper: Designing enzymes for new-to-nature chemistry and non-natural substrates with AlphaProtein Novo (bioRxiv) (external site: biorxiv.org)
Availability and license
Overall availability
Downloadable without sign-in from the Google Cloud Storage URL given in the README, under terms that allow only non-commercial use by or for non-commercial organizations and forbid republishing the weights. The terms also say Google may ask users to verify their name and organization.153
Availability is separate from permission: read the license before using or redistributing.
AlphaProtein Novo Generator Model Parameters Terms of Use (external site: github.com)31
Apache License 2.0 (external site: raw.githubusercontent.com)21
Plain-language guide to the Apache License 2.0
The README places the software under Apache 2.0, the generator weights under the AP Novo Generator Model Parameters Terms of Use (last modified 2026-10-05), generated designs under separate Output Terms of Use, and other materials under CC BY 4.0. The weights terms limit use to non-commercial work by or on behalf of non-commercial organizations, forbid publishing or sharing the weights outside the user's organization, forbid using outputs to train similar protein- or enzyme-design models, incorporate a prohibited use policy, and are revocable. The pipeline's AlphaFold 3 and AlphaFold 3 Leaving Atom weights are under the separate AlphaFold 3 model parameters terms, and LigandMPNN is under its own terms.134
Component reuse rights
- weights
- Unknown — no complete fact-level rights review
- code
- Reviewed qualifying license recorded — check scope and conditions
- data
- Unknown — no complete fact-level rights review
- documentation
- Unknown — no complete fact-level rights review
No complete system-rights review is recorded for this release.
Model-disclosure tier
The model parameters for this release can be downloaded by the public. License terms may still restrict use, redistribution, or commercial use.
The weights are under a license that is not on the rubric's OSI-approved list. Read its terms before use.
Public materials checklist
| Item | Status | Notes and evidence |
|---|---|---|
| WeightsCan the general public download the model parameters for this release? | Public | A single compressed weights file downloadable from Google Cloud Storage without sign-in; use is restricted to non-commercial purposes by the weights terms.153 |
| Inference codeIs code for running the model published? | Public | The repository provides run_generator.py for the diffusion model and run_pipeline.py for the end-to-end pipeline. The README describes the repository as a port of the original Google-internal pipeline used for the paper.1 |
| Training codeIs the code used to train the model published? | Unknown | The README covers generation, folding, and evaluation; no training code was found in it.1 |
| Training-data informationDoes the information cover provenance, scope, acquisition, selection, labeling, processing, and where data or alternatives can be obtained? Access alone does not establish completeness. | Unknown | The bioRxiv paper was not reviewed for this record. |
| Training-data accessCan the training data be obtained? This is independent of information completeness and reuse rights; original unshareable data need not be downloadable. | Unknown | Not assessed. |
| Complete training pipelineIs the complete base-training and preprocessing pipeline published, including configuration? Fine-tuning code or an inference SDK alone is insufficient. | Unknown | Not assessed. |
| Legacy data assessment (v0.1)Historical assessment combining download access and disclosure. Preserved for traceability; excluded from the v0.2 tier calculation. See the new separate assessments above. | Unknown | Not assessed. |
| Training recipeAre the training configuration and procedure documented in enough detail to follow? | Unknown | The bioRxiv paper was not reviewed for this record. |
| Evaluation materialsPublic = evaluation code or prompts that let others re-run the evaluations are published. Partial = results only. | Partial | The repository includes evaluate_design.py with evaluation suites for five example reactions and example manifests that partially reproduce the paper's settings; the README says some settings were reduced relative to the paper.1 |
What it is useful for
Run and use notes
Organization context
Other releases in the AlphaProtein Novo family
No other releases in this family have been assessed.
What this catalog does not know
- Training code: unknown.
- Training-data information: unknown.
- Training-data access: unknown.
- Complete training pipeline: unknown.
- Legacy data assessment (v0.1): unknown.
- Training recipe: unknown.
Have a primary source? How to report a correction.
U.S. eligibility
Eligible · basis: U.S.-governed project
The repository is published in Google DeepMind's GitHub organization, its licensing section states copyright 2026 Google LLC, and the weights terms are an agreement with Google LLC (Google Ireland Limited for users in the EEA or Switzerland) under California law. See the google-deepmind organization record, which is assessed under U.S. control as part of Google.13
Sources
This listing is not an endorsement, a safety assessment, or a federal approval.
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APA style
USASI. (2026, October 11). AlphaProtein Novo Generator. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/alphaprotein-novo-generator/
BibTeX
@misc{usasi_alphaprotein_novo_generator,
author = {{USASI}},
title = {AlphaProtein Novo Generator},
year = {2026},
month = oct,
howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/alphaprotein-novo-generator/}},
note = {United States of America Superintelligence. Last updated 2026-10-11}
}The date is when this page was last updated. Add the date you read it if your style needs one.
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