1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
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
|
