unimol-tools 0.1.0.post4

Description:

unimoltools 0.1.0.post4

Uni-Mol tools for various prediction and downstreams.
Documentation of Uni-Mol tools is available at https://unimol.readthedocs.io/en/latest/
Details can be found in bohrium notebook

unimol property predict
unimol representation

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)
pip install unimol_tools

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 UniMol models.
Option 2: Installing from source
## 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}
}
@misc{lu2023highly,
title={Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+},
author={Shuqi Lu and Zhifeng Gao and Di He and Linfeng Zhang and Guolin Ke},
year={2023},
eprint={2303.16982},
archivePrefix={arXiv},
primaryClass={physics.chem-ph}
}

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