This docker image builds the ImageMol [1] dictionary in numpy format for the given molecules.
Please save your molecules into a single smiles.csv file with only one column.
The title of this column should be SMILES and each row only contain one SMILES.
SMILES
C(=O)NCCCS(O)(=O)=O
CC(=O)NC1=CC=C(O)C=C1
NC12CC3CC(CC(C3)C1)C2
CC[N+](CC)(CCNC(=O)C(=O)NCC[N+](CC)(CC)CC1=CC=CC=C1Cl)CC1=CC=CC=C1Cl
CN(C)CCC=C1C2=CC=CC=C2CCC2=CC=CC=C12
CC(N)CC1=CC=CC=C1
Then run the following command to build the ImageMol dictionary for the given smiles.csv.
docker run --rm --gpus '"device=#"' --shm-size=8gb -v </local/directory/of/smiles.csv>:/data pykao/imagemol --input /data/smiles.csv
After running the above command, you are able to find the resulting ImageMol_dict.npy under </local/directory/of/smiles.csv>/ImageMol/
You are able to load this ImageMol dictionary using:
imagemol_dictionary = np.load('ImageMol_dict.npy',allow_pickle='TRUE').item()
Please kindly cite our work if you find this repository is helpful to you.
Kao, Po-Yu, et al. "Toward drug-target interaction prediction via ensemble modeling and transfer learning." 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021.
Kao, Po-Yu, et al. "Exploring the advantages of quantum generative adversarial networks in Generative Chemistry." Journal of Chemical Information and Modeling (2023).
ref: [1] Zeng, Xiangxiang, et al. "Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework." Nature Machine Intelligence 4.11 (2022): 1004-1016.
Content type
Image
Digest
sha256:b9eb1bc00…
Size
5.3 GB
Last updated
about 3 years ago
docker pull pykao/imagemol