# chiralfinder
Data and codes for the paper "ChiralFinder: an Open-Source Tool for Precise Stereogenic Element Detection and Stereoisomer Discrimination", in submission.
## Quick use
Install Anaconda, create and enter your own environment like
conda create -n env_test python=3.10
Enter the conda environment and install the ChiralFinder package through pip like
```
conda activate env_test
pip install chiralfinder
```
Run `test.py` to get example results.
```
python test.py
```
```python
from chiralfinder import ChiralFinder
if __name__ == '__main__':
smi_list = ["C[C@H]1CC(=O)[C@]2(CCCC2=O)C1", "CC1=CC=C(SC2=C(C)N(C3=CC=CC=C3C(C)(C)C)C(C)=C2)C=C1"]
chiral_finder = ChiralFinder(smi_list, "SMILES")
res_ = chiral_finder.get_axial(n_cpus=8)
print(res_[0]["chiral axes"], res_[1]["chiral axes"])
chiral_finder.draw_res_axial("./img")
smi_list_center = ["BrC/C(=C\[C@@H]1CCCO1)C1CCCCC1"]
chiral_finder = ChiralFinder(smi_list_center, "SMILES")
res_ = chiral_finder.get_central()
print(res_)
```
You will get the images of two molecules with predicted chiral axes in the folder `./img` by default. Predicted chiral axes:
```
[(5,)] [(9, 10)]
```
<img src="https://github.com/Meteor-han/chiralfinder/blob/main/img_axial/0.png" alt="0" width="30%" height="auto" /><img src="https://github.com/Meteor-han/chiralfinder/blob/main/img_axial/1.png" alt="1" width="30%" height="auto" />
You will get the prediction of one molecule for central chirality.
```
[{
'center id': [4],
'quadrupole matrix':
[[array([[-0.29989323, -1.08474687, 0.09943544],
[-2.0754821 , 0.47857598, 1.02051223],
[-0.0064714 , -0.03258116, 2.29906673]])]],
'determinant': [[-5.501797575969392]],
'sign': [[-1.0]]
}]
```
## The RotA dataset
The RotA dataset in excel with labeled chiral axes and some calculated molecular properties is stored in the folder `./data`.
## Citation
```
To be filled
```
Raw data
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"description": "# chiralfinder\n\nData and codes for the paper \"ChiralFinder: an Open-Source Tool for Precise Stereogenic Element Detection and Stereoisomer Discrimination\", in submission.\n\n## Quick use\n\nInstall Anaconda, create and enter your own environment like\n\n conda create -n env_test python=3.10\nEnter the conda environment and install the ChiralFinder package through pip like\n\n```\nconda activate env_test\npip install chiralfinder\n```\n\nRun `test.py` to get example results.\n\n```\npython test.py\n```\n\n```python\nfrom chiralfinder import ChiralFinder\n\nif __name__ == '__main__':\n smi_list = [\"C[C@H]1CC(=O)[C@]2(CCCC2=O)C1\", \"CC1=CC=C(SC2=C(C)N(C3=CC=CC=C3C(C)(C)C)C(C)=C2)C=C1\"]\n\n chiral_finder = ChiralFinder(smi_list, \"SMILES\")\n res_ = chiral_finder.get_axial(n_cpus=8)\n print(res_[0][\"chiral axes\"], res_[1][\"chiral axes\"])\n chiral_finder.draw_res_axial(\"./img\")\n\n smi_list_center = [\"BrC/C(=C\\[C@@H]1CCCO1)C1CCCCC1\"]\n chiral_finder = ChiralFinder(smi_list_center, \"SMILES\")\n res_ = chiral_finder.get_central()\n print(res_)\n```\n\nYou will get the images of two molecules with predicted chiral axes in the folder `./img` by default. Predicted chiral axes:\n\n```\n[(5,)] [(9, 10)]\n```\n\n<img src=\"https://github.com/Meteor-han/chiralfinder/blob/main/img_axial/0.png\" alt=\"0\" width=\"30%\" height=\"auto\" /><img src=\"https://github.com/Meteor-han/chiralfinder/blob/main/img_axial/1.png\" alt=\"1\" width=\"30%\" height=\"auto\" />\n\nYou will get the prediction of one molecule for central chirality.\n\n```\n[{\n'center id': [4], \n'quadrupole matrix': \n [[array([[-0.29989323, -1.08474687, 0.09943544],\n [-2.0754821 , 0.47857598, 1.02051223],\n [-0.0064714 , -0.03258116, 2.29906673]])]], \n'determinant': [[-5.501797575969392]], \n'sign': [[-1.0]]\n}]\n```\n\n\n\n\n## The RotA dataset\n\nThe RotA dataset in excel with labeled chiral axes and some calculated molecular properties is stored in the folder `./data`.\n\n## Citation\n\n```\nTo be filled\n```\n\n\n",
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