E3FP1 is a 3D molecular fingerprinting method inspired by Extended Connectivity FingerPrints (ECFP)2.
e3fp
is compatible with Python 2.7.x and 3.6.x. It additionally has the following
dependencies:
The following packages are required for the specified features:
- parallelization:
- molecular standardisation:
- protonation states:
- storing conformer energies:
The following installation approaches are listed in order of recommendation. Each of these approaches first requires an installation of RDKit.
- Install with
pip install e3fp
- To install the optional Python dependencies, run
pip install mpi4py futures standardiser h5py
- Install any of the optional dependencies above.
- Download this repository to your machine.
- Clone this repository to your machine with
git clone https://github.com/keiserlab/e3fp.git
. - OR download an archive by navigating to https://github.com/keiserlab/e3fp and clicking "Download ZIP". Extract the archive.
- Clone this repository to your machine with
- Install with
cd e3fp python setup.py install
After installation, it is recommended to run all tests with nose
,
pip install nose
nosetests e3fp
To use E3FP in a python script, enter:
import e3fp
See pipeline.py
for methods for generating conformers and E3FP fingerprints
from various inputs.
Run python e3fp/conformer/generate.py --help
for options for generating conformers.
Run python e3fp/fingerprint/generate.py --help
for options for generating E3FP fingerprints.
See defaults.cfg
for an example params file.
See the E3FP paper repo for an application of E3FP and all code used for the E3FP paper1.
- Axen SD, Huang XP, Caceres EL, Gendelev L, Roth BL, Keiser MJ. A Simple Representation Of Three-Dimensional Molecular Structure. bioRxiv (2017). doi: 10.1101/136705. (preprint)
- Rogers D & Hahn M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 50, 742-54 (2010). doi: 10.1021/ci100050t