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

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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

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 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

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