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