---
license: apache-2.0
tags:
  - drug-discovery
  - molecular-generation
  - protein-ligand
  - diffusion
  - flow-matching
---

# Force-Field-Guided Protein-Ligand Generation — Checkpoints

Model checkpoints accompanying the paper
**"Improving protein-ligand complex generation with force field guidance"**
(Lai et al., 2025).

- **Code:** https://github.com/wangxiaoyunNV/NV-AZ-DrugDiscovery
- **Paper:** https://openreview.net/forum?id=oGywgZ5dR8

## Contents

| Path | Size | Description |
| ---- | ---- | ----------- |
| `edm/conditional_model_updates_487_epochs.ckpt` | 21 MB | EDM backbone, **conditional** (protein-pocket aware). 487 training epochs. |
| `edm/model_updates_738999.ckpt` | 21 MB | EDM backbone, **unconditional** molecule generation. 738k training updates. |
| `semlaflow/model_1743387578_299_1265.pt` | 151 MB | SemlaFlow flow-matching backbone weights. |
| `semlaflow/chpt_1743387578_299_1265.pt` | 1 KB | SemlaFlow checkpoint metadata (hyperparameters / config). |

The SemlaFlow optimizer state (~452 MB) used for resuming training is
**not** included — it isn't needed to reproduce the paper results. Contact
the authors if you need it.

## Usage

From the project repo, run:

```bash
python download_checkpoints.py
```

Or, manually:

```bash
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="xiaoyunw/force-field-guidance-checkpoints",
    local_dir=".",
    local_dir_use_symlinks=False,
)
```

This places files under `edm/checkpoints/` and `semlaflow/checkpoints/`,
matching the paths expected by the generation scripts.

## License

Apache 2.0, matching the code repository.