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 Keras and Tensorflow (1.x), and makes heavy use of the neural fingerprint library.
For additional details, see the (upcoming) publication:
- St. John, P.C., Guan, Y., Kim, Y., Kim., S., and Paton, R.S., Prediction of homolytic bond dissociation enthalpies for organic molecules at near chemical accuracy with sub-second computational cost
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