%global _empty_manifest_terminate_build 0 Name: python-alfabet Version: 0.4.1 Release: 1 Summary: A library to estimate bond dissociation energies (BDEs) of organic molecules License: MIT License URL: https://github.com/NREL/alfabet Source0: https://mirrors.aliyun.com/pypi/web/packages/f0/51/0fac2d12ff586c42deea6785fdb7161a0f0ffdaee9c676a8699da25a8462/alfabet-0.4.1.tar.gz BuildArch: noarch Requires: python3-pandas Requires: python3-nfp Requires: python3-tqdm Requires: python3-pooch Requires: python3-joblib Requires: python3-scikit-learn %description ![ALFABET logo](/docs/logo.svg) [![PyPI version](https://badge.fury.io/py/alfabet.svg)](https://badge.fury.io/py/alfabet) [![Build Status](https://travis-ci.com/NREL/alfabet.svg?branch=master)](https://travis-ci.com/NREL/alfabet) # 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](https://bde.ml.nrel.gov/). The basic interface works as follows, where `predict` expects a list of SMILES strings of the target molecules ```python >>> 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](github.com/NREL/nfp) 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`](https://github.com/rdkit/rdkit) can otherwise be difficult to install ```bash $ conda create -n alfabet -c conda-forge python=3.7 rdkit $ source activate alfabet $ pip install alfabet `` %package -n python3-alfabet Summary: A library to estimate bond dissociation energies (BDEs) of organic molecules Provides: python-alfabet BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-alfabet ![ALFABET logo](/docs/logo.svg) [![PyPI version](https://badge.fury.io/py/alfabet.svg)](https://badge.fury.io/py/alfabet) [![Build Status](https://travis-ci.com/NREL/alfabet.svg?branch=master)](https://travis-ci.com/NREL/alfabet) # 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](https://bde.ml.nrel.gov/). The basic interface works as follows, where `predict` expects a list of SMILES strings of the target molecules ```python >>> 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](github.com/NREL/nfp) 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`](https://github.com/rdkit/rdkit) can otherwise be difficult to install ```bash $ conda create -n alfabet -c conda-forge python=3.7 rdkit $ source activate alfabet $ pip install alfabet `` %package help Summary: Development documents and examples for alfabet Provides: python3-alfabet-doc %description help ![ALFABET logo](/docs/logo.svg) [![PyPI version](https://badge.fury.io/py/alfabet.svg)](https://badge.fury.io/py/alfabet) [![Build Status](https://travis-ci.com/NREL/alfabet.svg?branch=master)](https://travis-ci.com/NREL/alfabet) # 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](https://bde.ml.nrel.gov/). The basic interface works as follows, where `predict` expects a list of SMILES strings of the target molecules ```python >>> 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](github.com/NREL/nfp) 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`](https://github.com/rdkit/rdkit) can otherwise be difficult to install ```bash $ conda create -n alfabet -c conda-forge python=3.7 rdkit $ source activate alfabet $ pip install alfabet `` %prep %autosetup -n alfabet-0.4.1 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-alfabet -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Jun 20 2023 Python_Bot - 0.4.1-1 - Package Spec generated