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xiaoyunw/force-field-guidance-checkpoints

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face
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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

PathSizeDescription
edm/conditional_model_updates_487_epochs.ckpt21 MBEDM backbone, conditional (protein-pocket aware). 487 training epochs.
edm/model_updates_738999.ckpt21 MBEDM backbone, unconditional molecule generation. 738k training updates.
semlaflow/model_1743387578_299_1265.pt151 MBSemlaFlow flow-matching backbone weights.
semlaflow/chpt_1743387578_299_1265.pt1 KBSemlaFlow 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.