unimol_tools

Uni-Mol tools for various prediction and downstreams.

Documentation of Uni-Mol tools is available at https://unimol.readthedocs.io/en/latest/

Install

• pytorch is required, please install pytorch according to your environment. if you are using cuda, please install pytorch with cuda. More details can be found at https://pytorch.org/get-started/locally/
• currently, rdkit needs with numpy<2.0.0, please install rdkit with numpy<2.0.0.

Option 1: Installing from PyPi (Recommended, for stable version)

pip install unimol_tools --upgrade

We recommend installing huggingface_hub so that the required unimol models can be automatically downloaded at runtime! It can be install by

pip install huggingface_hub

huggingface_hub allows you to easily download and manage models from the Hugging Face Hub, which is key for using Uni-Mol models.

Option 2: Installing from source (for latest version)

## Dependencies installation
pip install -r requirements.txt

## Clone repository
git clone https://github.com/deepmodeling/Uni-Mol.git
cd Uni-Mol/unimol_tools

## Install
python setup.py install

Models in Huggingface

The UniMol pretrained models can be found at dptech/Uni-Mol-Models.

If the download is slow, you can use other mirrors, such as:

export HF_ENDPOINT=https://hf-mirror.com

Setting the HF_ENDPOINT environment variable specifies the mirror address for the Hugging Face Hub to use when downloading models.

Modify the default directory for weights

Setting the UNIMOL_WEIGHT_DIR environment variable specifies the directory for pre-trained weights if the weights have been downloaded from another source.

export UNIMOL_WEIGHT_DIR=/path/to/your/weights/dir/

News

• 2024-07-23: User experience improvements: Add UNIMOL_WEIGHT_DIR.
• 2024-06-25: unimol_tools has been publish to pypi! Huggingface has been used to manage the pretrain models.
• 2024-06-20: unimol_tools v0.1.0 released, we remove the dependency of Uni-Core. And we will publish to pypi soon.
• 2024-03-20: unimol_tools documents is available at https://unimol.readthedocs.io/en/latest/

molecule property prediction

from unimol_tools import MolTrain, MolPredict
clf = MolTrain(task='classification', 
                data_type='molecule', 
                epochs=10, 
                batch_size=16, 
                metrics='auc',
                )
pred = clf.fit(data = data)
# currently support data with smiles based csv/txt file, and
# custom dict of {'atoms':[['C','C],['C','H','O']], 'coordinates':[coordinates_1,coordinates_2]}

clf = MolPredict(load_model='../exp')
res = clf.predict(data = data)

unimol molecule and atoms level representation

import numpy as np
from unimol_tools import UniMolRepr
# single smiles unimol representation
clf = UniMolRepr(data_type='molecule', remove_hs=False)
smiles = 'c1ccc(cc1)C2=NCC(=O)Nc3c2cc(cc3)[N+](=O)[O]'
smiles_list = [smiles]
unimol_repr = clf.get_repr(smiles_list, return_atomic_reprs=True)
# CLS token repr
print(np.array(unimol_repr['cls_repr']).shape)
# atomic level repr, align with rdkit mol.GetAtoms()
print(np.array(unimol_repr['atomic_reprs']).shape)

Please kindly cite our papers if you use the data/code/model.

@inproceedings{
  zhou2023unimol,
  title={Uni-Mol: A Universal 3D Molecular Representation Learning Framework},
  author={Gengmo Zhou and Zhifeng Gao and Qiankun Ding and Hang Zheng and Hongteng Xu and Zhewei Wei and Linfeng Zhang and Guolin Ke},
  booktitle={The Eleventh International Conference on Learning Representations },
  year={2023},
  url={https://openreview.net/forum?id=6K2RM6wVqKu}
}
@article{gao2023uni,
  title={Uni-qsar: an auto-ml tool for molecular property prediction},
  author={Gao, Zhifeng and Ji, Xiaohong and Zhao, Guojiang and Wang, Hongshuai and Zheng, Hang and Ke, Guolin and Zhang, Linfeng},
  journal={arXiv preprint arXiv:2304.12239},
  year={2023}
}

License

This project is licensed under the terms of the MIT license. See LICENSE for additional details.