---
language: en
license: mit
tags:
  - protein-design
  - sequence-design
  - jax
  - equinox
  - protein-mpnn
  - ligand-mpnn
library_name: aminx
---

# aminx weights — v0.1.0a1

JAX/Equinox reimplementation of [ProteinMPNN](https://github.com/dauparas/ProteinMPNN) and [LigandMPNN](https://github.com/dauparas/LigandMPNN).

Weights are converted from the original PyTorch checkpoints and verified to ≥0.999 Pearson correlation with the reference implementation (atol/rtol 1e-4 for all families; side-chain packer: atol 1e-4/rtol 1e-3).

## Usage

```bash
pip install aminx   # huggingface-hub is a required dep; weights download on first use
```

```python
from aminx.io.weights import load_model

model = load_model("proteinmpnn_v_48_020")
model = load_model("ligandmpnn_v_32_020_25")
model = load_model("solublempnn_v_48_020")
model = load_model("per_residue_label_membrane_mpnn_v_48_020")
model = load_model("ligandmpnn_sc_v_32_002_16")
```

Weights are cached to `~/.cache/huggingface/hub/` after first download.

## Checkpoint topology

All families share: node/edge/hidden features = 128, encoder layers = 3, decoder layers = 3, vocab size = 21. The suffix digits in checkpoint names encode training noise level (e.g. `_020` = 0.20 Å backbone noise, per upstream convention) and atom context count (e.g. `_25`, `_16`).

| Family | Checkpoints | k_neighbors | num_positional_embeddings | Notes |
|---|---|---|---|---|
| ProteinMPNN | `proteinmpnn_v_48_{002,010,020,030}` | 48 | 32 | Original backbone |
| SolubleMPNN | `solublempnn_v_48_{002,010,020,030}` | 48 | 32 | Solubility-biased variant |
| LigandMPNN | `ligandmpnn_v_32_{005,010,020,030}_25` | 32 | 32 | 2 ligand-context layers; atom_context_num=25 |
| Membrane | `{global,per_residue}_label_membrane_mpnn_v_48_020` | 48 | 32 | physics_feature_dim=3 |
| Side-chain packer | `ligandmpnn_sc_v_32_002_16` | 32 | 16 | atom_context_num=16 |

## Deprecation notice

The previous `maraxen/prxteinmpnn` repository is archived. Use this repo going forward.

## Citation

If you use these weights, please cite the original works:

```bibtex
@article{dauparas2022robust,
  title={Robust deep learning-based protein sequence design using ProteinMPNN},
  author={Dauparas, Justas and others},
  journal={Science},
  year={2022}
}

@article{dauparas2025atomic,
  title={Atomic context-conditioned protein sequence design using LigandMPNN},
  author={Dauparas, Justas and others},
  journal={Nature Methods},
  volume={22},
  number={4},
  pages={717--723},
  year={2025}
}
```