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xiaoyunw/force-field-guidance-checkpoints
force-field-guidance-checkpoints is a machine learning model from xiaoyunw. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Model checkpoints accompanying the paper "Improving protein-ligand complex generation with force field guidance" (Lai et al., 2025).
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Updated Apr 24, 2026
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From the Hugging Face model README
Model checkpoints accompanying the paper "Improving protein-ligand complex generation with force field guidance" (Lai et al., 2025).
| 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.
From the project repo, run:
python download_checkpoints.py
Or, manually:
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.
Apache 2.0, matching the code repository.