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        "author": "Peter St. John",
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        "description": "![ALFABET logo](/docs/logo.svg)\n\n[![PyPI version](https://badge.fury.io/py/alfabet.svg)](https://badge.fury.io/py/alfabet)\n[![Build Status](https://travis-ci.com/NREL/alfabet.svg?branch=master)](https://travis-ci.com/NREL/alfabet)\n\n# A machine-Learning derived, Fast, Accurate Bond dissociation Enthalpy Tool (ALFABET)\n\nThis 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](https://bde.ml.nrel.gov/).\n\nThe basic interface works as follows, where `predict` expects a list of SMILES strings of the target molecules\n```python\n>>> from alfabet import model\n>>> model.predict(['CC', 'NCCO'])\n```\n```\n  molecule  bond_index bond_type fragment1 fragment2  ...    bde_pred  is_valid\n0       CC           0       C-C     [CH3]     [CH3]  ...   90.278282      True\n1       CC           1       C-H       [H]    [CH2]C  ...   99.346184      True\n2     NCCO           0       C-N   [CH2]CO     [NH2]  ...   89.988495      True\n3     NCCO           1       C-C    [CH2]O    [CH2]N  ...   82.122429      True\n4     NCCO           2       C-O   [CH2]CN      [OH]  ...   98.250961      True\n5     NCCO           3       H-N       [H]   [NH]CCO  ...   99.134750      True\n6     NCCO           5       C-H       [H]   N[CH]CO  ...   92.216087      True\n7     NCCO           7       C-H       [H]   NC[CH]O  ...   92.562988      True\n8     NCCO           9       H-O       [H]    NCC[O]  ...  105.120598      True\n```\n\nThe 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. \nThe model is based on Tensorflow (2.x), and makes heavy use of the [neural fingerprint](github.com/NREL/nfp) library (0.1.x).\n\nFor additional details, see the publication:\nSt. 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\n\n*Note:* For the exact model described in the text, install `alfabet` version 0.0.x. Versions >0.1 have been updated for tensorflow 2.\n\n## Installation\nInstallation with `conda` is recommended, as [`rdkit`](https://github.com/rdkit/rdkit) can otherwise be difficult to install\n\n```bash\n$ conda create -n alfabet -c conda-forge python=3.7 rdkit\n$ source activate alfabet\n$ pip install alfabet\n``\n\n\n",
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