Self-Assembling-Amyloid-Like-Peptides-Predictor

unimol 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

• Notice: Uni-Core is needed, please install it first. Current Uni-Core requires torch>=2.0.0 by default, if you want to install other version, please check its Installation Documentation.

## unicore and other dependencies installation
pip install -r requirements.txt
## clone repo
git clone https://github.com/dptech-corp/Uni-Mol.git
cd Uni-Mol/unimol_tools/unimol_tools

## download pretrained weights
wget https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/mol_pre_all_h_220816.pt
wget https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/mol_pre_no_h_220816.pt
wget https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/pocket_pre_220816.pt
wget https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/mof_pre_no_h_CORE_MAP_20230505.pt
wget https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/mp_all_h_230313.pt
wget https://github.com/dptech-corp/Uni-Mol/releases/download/v0.1/oled_pre_no_h_230101.pt

mkdir -p weights
mv *.pt weights/

## install
cd ..
python setup.py install

News

• unimol_tools documents is coming soon.

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.