ATOM3D enables machine learning on three-dimensional molecular structure.
- Access to several datasets involving 3D molecular structure.
- LMDB data format for storing lots of molecules (and associated metadata).
- Utilities for splitting/filtering data based on many criteria.
For more detailed information, read the documentation.
Install with:
pip install atom3d
To use rdkit functionality, please install within conda:
conda create -n atom3d python=3.6 pip rdkit
conda activate atom3d
pip install atom3d
From python:
import atom3d.datasets as da
da.download_dataset('lba', PATH_TO_DATASET) # Download LBA dataset.
Or, download and unzip from the website.
From python:
import atom3d.datasets as da
dataset = da.load_dataset(PATH_TO_DATASET, {'lmdb','pdb','silent','sdf','xyz','xyz-gdb'})
print(len(dataset)) # Print length
print(dataset[0].keys()) # Print keys
LMDB allows for compressed, fast, random access to your structures, all within a single database. Currently, we support creating LMDB datasets from PDB files, silent files, and xyz files.
From command line:
python -m atom3d.datasets PATH_TO_PDB_DIR PATH_TO_DATASET --filetype {pdb,silent,xyz,xyz-gdb}
For more usage, please see the documentation.
As a living repository, we welcome contributions of additional datasets, methods, and functionality! See the Contributing section of the documentation for details.
For support, please file an issue at https://github.com/drorlab/atom3d/issues.
The project is licensed under the MIT license.
We provide an overview on ATOM3D and details on the preparation of all datasets in our preprint:
R. J. L. Townshend, M. Vögele, P. Suriana, A. Derry, A. Powers, Y. Laloudakis, S. Balachandar, B. Jing, B. Anderson, S. Eismann, R. Kondor, R. B. Altman, R. O. Dror "ATOM3D: Tasks On Molecules in Three Dimensions", arXiv:2012.04035
Please cite this work if some of the ATOM3D code or datasets are helpful in your scientific endeavours. For specific datasets, please also cite the respective original source(s), given in the preprint.