Skip to main content

A library to estimate bond dissociation energies (BDEs) of organic molecules

Project description

ALFABET logo

PyPI version Build Status

A machine-Learning derived, Fast, Accurate Bond dissociation Enthalpy Tool (ALFABET)

This library contains the trained graph neural network model for the prediction of homolytic bond dissociation energies (BDEs) of organic molecules with C, H, N, and O atoms. This package offers a command-line interface to the web-based model predictions at bde.ml.nrel.gov.

The basic interface works as follows, where predict expects a list of SMILES strings of the target molecules

>>> from alfabet import model
>>> model.predict(['CC', 'NCCO'])
  molecule  bond_index bond_type fragment1 fragment2  ...    bde_pred  is_valid
0       CC           0       C-C     [CH3]     [CH3]  ...   90.278282      True
1       CC           1       C-H       [H]    [CH2]C  ...   99.346184      True
2     NCCO           0       C-N   [CH2]CO     [NH2]  ...   89.988495      True
3     NCCO           1       C-C    [CH2]O    [CH2]N  ...   82.122429      True
4     NCCO           2       C-O   [CH2]CN      [OH]  ...   98.250961      True
5     NCCO           3       H-N       [H]   [NH]CCO  ...   99.134750      True
6     NCCO           5       C-H       [H]   N[CH]CO  ...   92.216087      True
7     NCCO           7       C-H       [H]   NC[CH]O  ...   92.562988      True
8     NCCO           9       H-O       [H]    NCC[O]  ...  105.120598      True

The model breaks all single, non-cyclic bonds in the input molecules and calculates their bond dissociation energies. Typical prediction errors are less than 1 kcal/mol. The model is based on Tensorflow (2.x), and makes heavy use of the neural fingerprint library (0.1.x).

For additional details, see the publication: St. John, P. C., Guan, Y., Kim, Y., Kim, S., & Paton, R. S. (2020). Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost. Nature Communications, 11(1). doi:10.1038/s41467-020-16201-z

Note: For the exact model described in the text, install alfabet version 0.0.x. Versions >0.1 have been updated for tensorflow 2.

Installation

Installation with conda is recommended, as rdkit can otherwise be difficult to install

$ conda create -n alfabet -c conda-forge python=3.7 rdkit
$ source activate alfabet
$ pip install alfabet
``

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

alfabet-0.4.1.tar.gz (28.5 kB view details)

Uploaded Source

Built Distribution

alfabet-0.4.1-py3-none-any.whl (12.1 kB view details)

Uploaded Python 3

File details

Details for the file alfabet-0.4.1.tar.gz.

File metadata

  • Download URL: alfabet-0.4.1.tar.gz
  • Upload date:
  • Size: 28.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.8.13

File hashes

Hashes for alfabet-0.4.1.tar.gz
Algorithm Hash digest
SHA256 a91fe728a650406eaa521956577cf1222a7a3cd4b78557956db1483575447e65
MD5 e4818146a2a9293d0597cd289d44a40c
BLAKE2b-256 f0510fac2d12ff586c42deea6785fdb7161a0f0ffdaee9c676a8699da25a8462

See more details on using hashes here.

File details

Details for the file alfabet-0.4.1-py3-none-any.whl.

File metadata

  • Download URL: alfabet-0.4.1-py3-none-any.whl
  • Upload date:
  • Size: 12.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.8.13

File hashes

Hashes for alfabet-0.4.1-py3-none-any.whl
Algorithm Hash digest
SHA256 c77f1d7085980030fbcccad2ac362c281b96f5840ba08d53608b551f11076ed1
MD5 e4a6b4acc84c75dd75d4b34978623293
BLAKE2b-256 b44f0839bb23b31d38a2f3331dc400f1785cb29700b281e7b388154b869dc090

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page