| Name | molcraft JSON |
| Version |
0.1.0a11
JSON |
| download |
| home_page | None |
| Summary | Graph Neural Networks for Molecular Machine Learning |
| upload_time | 2025-09-08 09:46:46 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License
Copyright (c) 2025 Alexander Kensert
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
python
machine-learning
deep-learning
graph-neural-networks
molecular-machine-learning
molecular-graphs
computational-chemistry
computational-biology
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<img src="https://github.com/akensert/molcraft/blob/main/docs/_static/molcraft-logo.png" alt="molcraft-logo">
**Deep Learning on Molecules**: A Minimalistic GNN package for Molecular ML.
> [!NOTE]
> In progress.
## Installation
For CPU users:
```bash
pip install --pre molcraft
```
For GPU users:
```bash
pip install --pre molcraft[gpu]
```
## Examples
```python
from molcraft import features
from molcraft import descriptors
from molcraft import featurizers
from molcraft import layers
from molcraft import models
import keras
featurizer = featurizers.MolGraphFeaturizer(
atom_features=[
features.AtomType(),
features.NumHydrogens(),
features.Degree(),
],
bond_features=[
features.BondType(),
features.IsRotatable(),
],
super_atom=True,
self_loops=True,
)
graph = featurizer([('N[C@@H](C)C(=O)O', 2.0), ('N[C@@H](CS)C(=O)O', 1.0)])
print(graph)
model = models.GraphModel.from_layers(
[
layers.Input(graph.spec),
layers.NodeEmbedding(dim=128),
layers.EdgeEmbedding(dim=128),
layers.GraphTransformer(units=128),
layers.GraphTransformer(units=128),
layers.GraphTransformer(units=128),
layers.GraphTransformer(units=128),
layers.Readout(mode='mean'),
keras.layers.Dense(units=1024, activation='relu'),
keras.layers.Dense(units=1024, activation='relu'),
keras.layers.Dense(1)
]
)
pred = model(graph)
print(pred)
# featurizers.save_featurizer(featurizer, '/tmp/featurizer.json')
# models.save_model(model, '/tmp/model.keras')
# loaded_featurizer = featurizers.load_featurizer('/tmp/featurizer.json')
# loaded_model = models.load_model('/tmp/model.keras')
```
Raw data
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"author_email": "Alexander Kensert <alexander.kensert@gmail.com>",
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"description": "<img src=\"https://github.com/akensert/molcraft/blob/main/docs/_static/molcraft-logo.png\" alt=\"molcraft-logo\">\n\n**Deep Learning on Molecules**: A Minimalistic GNN package for Molecular ML. \n\n> [!NOTE] \n> In progress.\n\n## Installation\n\nFor CPU users:\n\n```bash\npip install --pre molcraft\n```\n\nFor GPU users:\n```bash\npip install --pre molcraft[gpu]\n```\n\n## Examples \n\n```python\nfrom molcraft import features\nfrom molcraft import descriptors\nfrom molcraft import featurizers \nfrom molcraft import layers\nfrom molcraft import models \nimport keras\n\nfeaturizer = featurizers.MolGraphFeaturizer(\n atom_features=[\n features.AtomType(),\n features.NumHydrogens(),\n features.Degree(),\n ],\n bond_features=[\n features.BondType(),\n features.IsRotatable(),\n ],\n super_atom=True,\n self_loops=True,\n)\n\ngraph = featurizer([('N[C@@H](C)C(=O)O', 2.0), ('N[C@@H](CS)C(=O)O', 1.0)])\nprint(graph)\n\nmodel = models.GraphModel.from_layers(\n [\n layers.Input(graph.spec),\n layers.NodeEmbedding(dim=128),\n layers.EdgeEmbedding(dim=128),\n layers.GraphTransformer(units=128),\n layers.GraphTransformer(units=128),\n layers.GraphTransformer(units=128),\n layers.GraphTransformer(units=128),\n layers.Readout(mode='mean'),\n keras.layers.Dense(units=1024, activation='relu'),\n keras.layers.Dense(units=1024, activation='relu'),\n keras.layers.Dense(1)\n ]\n)\n\npred = model(graph)\nprint(pred)\n\n# featurizers.save_featurizer(featurizer, '/tmp/featurizer.json')\n# models.save_model(model, '/tmp/model.keras')\n\n# loaded_featurizer = featurizers.load_featurizer('/tmp/featurizer.json')\n# loaded_model = models.load_model('/tmp/model.keras')\n```\n\n",
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