RMSD-Pred

Protein-ligand binding pose RMSD prediction using Graph Neural Networks.

Installation

git clone https://github.com/eightmm/RMSD-Pred.git
cd RMSD-Pred
pip install -e .
pip install dgl -f https://data.dgl.ai/wheels/torch-2.4/cu121/repo.html

Usage

Command Line

rmsdpred \
    -r example/prot.pdb \
    -l example/ligs.sdf \
    -o results.tsv \
    --device cuda

Python API

from rmsdpred.inference import inference

# Uses packaged random_seed0 weights by default
inference(
    protein_pdb="example/prot.pdb",
    ligand_file="example/ligs.sdf",
    output="results.tsv",
    batch_size=128,
    device="cuda"
)

# Or specify custom weights
inference(
    protein_pdb="example/prot.pdb",
    ligand_file="example/ligs.sdf",
    output="results.tsv",
    batch_size=128,
    reg_weight="/path/to/reg.pth",
    cls_weight="/path/to/cls.pth",
    device="cuda"
)

Parameters

Parameter	Description	Default
-r, --protein_pdb	Receptor protein PDB file	required
-l, --ligand_file	Ligand file (.sdf/.mol2/.dlg/.pdbqt/.txt)	required
-o, --output	Output TSV file	result.tsv
--reg_weight	Regression model weight file	packaged random_reg_seed0.pth
--cls_weight	Classification model weight file	packaged random_cls_seed0.pth
--batch_size	Batch size	128
--device	cuda or cpu	cuda
--ncpu	Number of CPU workers	4

Output

Tab-separated file with columns:

• Name: Ligand pose identifier
• pRMSD: Predicted RMSD (Angstrom)
• Is_Above_2A: Confidence score (0-1, probability of RMSD > 2A)
• ADG_Score: AutoDock score (NaN if unavailable)

Project Structure

RMSD-Pred/
├── src/rmsdpred/
│   ├── data/
│   │   ├── data.py
│   │   ├── ligand_atom_feature.py
│   │   ├── protein_atom_feature.py
│   │   └── utils.py
│   ├── model/
│   │   ├── GatedGCNLSPE.py
│   │   └── model.py
│   ├── weight/
│   │   └── random/
│   └── inference.py
├── example/
│   ├── prot.pdb
│   ├── ligs.sdf
│   └── run.sh
├── pyproject.toml
└── README.md

Citation

@article{sim2026bapred,
  title     = {BA-Pred and RMSD-Pred: Integrated Graph Neural Network Models for Accurate Protein--Ligand Binding Affinity and Binding Pose Prediction},
  author    = {Sim, Jaemin and Lee, Juyong},
  journal   = {Journal of Chemical Information and Modeling},
  year      = {2026},
  doi       = {10.1021/acs.jcim.5c02591},
  publisher = {American Chemical Society (ACS)}
}

License

Apache License 2.0