This repository contains the code for Nesso-1, a coarse-grained cofolding model that can predict binding affinity. For more details about the method and evaluations, refer to this Technical Report. This project is licensed under a permissive Apache License 2.0.
PyPI release coming soon!
pip install "git+https://github.com/recursionpharma/nesso.git"GPU / CUDA : The nesso package relies on PyTorch. For GPU support, install a CUDA-enabled PyTorch build first, then install nesso. To additionally enable cuEquivariance kernels for extra speedups (CUDA 12 only; Apache License 2.0):
pip install "nesso[kernels] @ git+https://github.com/recursionpharma/nesso.git"If the kernels extra fails to install or you are running on CPU, use the default install and/or pass --no_kernels at runtime.
We recommend using uv to install the project in development mode.
git clone https://github.com/recursionpharma/nesso.git && cd nesso
uv sync # runtime + dev tools (pytest, ruff, pre-commit)
pytest # run the CPU-only test suite
pre-commit run --all-files # lint + format(Plain pip install -e . installs the runtime only; the dev tools live in the
dev dependency group, which uv sync installs.)
1. Create an input YAML (e.g. complex.yaml):
sequences:
- protein:
id: A
sequence: MKTAYIAKQ... # amino acid sequence
- ligand:
id: B
smiles: "Fc1ccc(cc1)C(=O)Nc1ccc(cc1)S(=O)(=O)N"
properties:
- affinity:
binder: B2. Run prediction:
nesso predict complex.yaml --out_dir ./outputNote: For input formats, caching, and all CLI options, see docs/prediction.md.
- Pocket Conditioning
- Structural Templating
@article{shenoy2026nesso1,
author = {Shenoy, Nikhil and Errington, David and Bengio, Emmanuel and Kapu\'sniak, Kacper and Klaeser, Kerstin and Pang, Yui Tik and Radenkovic, Vladimir and Tossou, Prudencio and Bois, Therence and Wedlake, Andrew and Di Giovanni, Francesco},
title = {Nesso-1: Accelerating Open-Source Binding Affinity Predictions},
year = {2026},
url = {https://www.biorxiv.org/content/10.64898/2026.08.01.742196v1},
doi = {10.64898/2026.08.01.742196},
journal = {bioRxiv}
}This project is licensed under Apache License 2.0.
Third-party attributions: licenses/THIRD_PARTY_NOTICES.md
We thank the following open-source codebases for enabling the development of this project,