https://github.com/grimme-lab/confrankplus
ConfRank+: Extending Conformer Ranking to Charged Molecules
https://github.com/grimme-lab/confrankplus
ase atomistic-machine-learning atomistic-modelling censo charge-model chemoinformatics computational-chemistry conformer-scoring conformer-search crest energy-prediction graph-neural-network machine-learning organic-chemistry pairwise-training pytorch torchscript
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ConfRank+: Extending Conformer Ranking to Charged Molecules
- Host: GitHub
- URL: https://github.com/grimme-lab/confrankplus
- Owner: grimme-lab
- License: other
- Created: 2025-05-19T16:18:37.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-06-05T09:28:44.000Z (7 months ago)
- Last Synced: 2025-06-05T10:30:33.059Z (7 months ago)
- Topics: ase, atomistic-machine-learning, atomistic-modelling, censo, charge-model, chemoinformatics, computational-chemistry, conformer-scoring, conformer-search, crest, energy-prediction, graph-neural-network, machine-learning, organic-chemistry, pairwise-training, pytorch, torchscript
- Language: Python
- Homepage:
- Size: 2.46 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
ConfRank+: Extending Conformer Ranking to Charged Molecules
A High-Throughput Machine Learning Model for Charged Molecular Conformers
Preprint |
Dataset |
ConfRank 1.0
# Installation
You can install the python code for running the ConfRankPlus model via pip:
```bash
pip3 install git+https://github.com/grimme-lab/confrankplus.git
```
It is **highly recommended** to install `torch_cluster` for significant faster computation of atomistic graphs, e.g., by
running:
```bash
pip install git+https://github.com/rusty1s/pytorch_cluster.git
```
For further installation guidelines, see [here](https://github.com/rusty1s/pytorch_cluster/tree/master).
# Inference
## Label xyz files from command line
You can label (multiple) xyz files (or other formats supported by the `ase.io` module) with the `confrankplus` command
in your command line:
```bash
confrankplus --files *.xyz --output_path confrank_output.xyz --total_charge 0 --fidelity r2SCAN-3c --batch_size 20
```
The results will be written to the path specified via `--output_path`. The units of the energies written to the output
path will be in kcal/mol.
Furthermore, in the directory `evaluate` there are example scripts for running inference on the test datasets of our
paper.
## ASE Calculator
In addition to the `confrankplus` command line interface, we provide an ASE calculator.
Example:
```python
import numpy as np
np.random.seed(0)
from ase.collections import s22
from ConfRankPlus.inference.calculator import ConfRankPlusCalculator
# load the ConfRankPlus calculator with r2SCAN-3c fidelity
calculator = ConfRankPlusCalculator.load_default(fidelity="r2SCAN-3c",
compute_forces=False)
molecule = s22['Adenine-thymine_Watson-Crick_complex']
molecule.set_calculator(calculator)
# energy before displacements:
energy_1 = molecule.get_potential_energy()
# Apply random displacements:
displacements = np.random.uniform(-0.01, 0.01, molecule.positions.shape) # in Angstrom
molecule.set_positions(molecule.get_positions() + displacements)
# energy after displacement:
energy_2 = molecule.get_potential_energy()
# ASE uses eV as unit for energy:
print(f"The energy difference is {energy_2 - energy_1:.2E} eV.")
```
Note: our model has not been tested for geometry optimization or MD simulations.
# Loading datasets
You can load the datasets from [Zenodo](https://zenodo.org/records/15465665) as follows:
```python
from ConfRankPlus.data.dataset import HDF5Dataset
filepath = ... # path to .h5 file
dataset = HDF5Dataset.from_hdf5(filepath=filepath, precision=64)
```
`dataset` will be an instance of the `InMemoryDataset` class from PyTorch Geometric.
# Citation
When using or referring to ConfRankPlus, please cite it as follows:
```
@misc{ConfRankPlus25,
author={Oerder, Rick and H{\"o}lzer , Christian and Hamaekers, Jan},
title={ConfRank+: Extending Conformer Ranking to Charged Molecules},
year={2025},
doi={10.26434/chemrxiv-2025-xkwk6},
note={ChemRxiv Preprint}}
```
and
```
@article{ConfRank24,
author = {H{\"o}lzer, Christian and Oerder, Rick and Grimme, Stefan and Hamaekers, Jan},
title = {ConfRank: Improving GFN-FF Conformer Ranking with Pairwise Training},
journal = {Journal of Chemical Information and Modeling},
volume = {64},
number = {23},
pages = {8909-8925},
year = {2024},
doi = {10.1021/acs.jcim.4c01524},
note ={PMID: 39565928},
URL = {https://doi.org/10.1021/acs.jcim.4c01524},
eprint = {https://doi.org/10.1021/acs.jcim.4c01524}}
```
# License
The content of this repository is licensed under CC BY-NC-SA 4.0
