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@@ -0,0 +1 @@ +/alfabet-0.4.1.tar.gz diff --git a/python-alfabet.spec b/python-alfabet.spec new file mode 100644 index 0000000..bf157a6 --- /dev/null +++ b/python-alfabet.spec @@ -0,0 +1,207 @@ +%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 + + +[](https://badge.fury.io/py/alfabet) +[](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 + + +[](https://badge.fury.io/py/alfabet) +[](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 + + +[](https://badge.fury.io/py/alfabet) +[](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 <Python_Bot@openeuler.org> - 0.4.1-1 +- Package Spec generated @@ -0,0 +1 @@ +e4818146a2a9293d0597cd289d44a40c alfabet-0.4.1.tar.gz |