chiralfinder 0.0.1

The chiralfinder package.

chiralfinder

Data and codes for the paper "ChiralFinder: A Computational Framework for Identifying Stereogenic Elements and Distinguishing Stereoisomers in Complex Molecules", 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

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)]

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]]
}]

Dataset

The RotA dataset is stored in the folder ./data. The excel file contains labeled chiral axes and some calculated molecular properties. The pickle file includes calculated molecular conformers.

We also provide sampled achiral molecules and centrally chiral molecules with multiple centers from the PubChem3D database in the folder ./data.

Citation

To be filled