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Open Source Science

https://github.com/compomics/molcraft

Deep Learning on Molecules.

https://github.com/compomics/molcraft

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
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    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (6.0%) to scientific vocabulary

Keywords

deep-learning graph-neural-network keras machine-learning molecular-machine-learning python tensorflow
Last synced: 6 months ago · JSON representation

Repository

Deep Learning on Molecules.

Basic Info
  • Host: GitHub
  • Owner: CompOmics
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 467 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
deep-learning graph-neural-network keras machine-learning molecular-machine-learning python tensorflow
Created 11 months ago · Last pushed 6 months ago
Metadata Files
Readme Changelog License

README.md

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( atomfeatures=[ features.AtomType(), features.NumHydrogens(), features.Degree(), ], bondfeatures=[ features.BondType(), features.IsRotatable(), ], superatom=True, selfloops=True, )

graph = featurizer([('NC@@HC(=O)O', 2.0), ('NC@@HC(=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')

loadedfeaturizer = featurizers.loadfeaturizer('/tmp/featurizer.json')

loadedmodel = models.loadmodel('/tmp/model.keras')

```

Owner

  • Name: Computational Omics and Systems Biology Group
  • Login: CompOmics
  • Kind: organization
  • Email: compomics.list@gmail.com

The CompOmics group, headed by Prof. Dr. Lennart Martens, specializes in the management, analysis and integration of high-throughput Omics data.

GitHub Events

Total
  • Push event: 1
Last Year
  • Push event: 1

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 86
  • Total Committers: 1
  • Avg Commits per committer: 86.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 86
  • Committers: 1
  • Avg Commits per committer: 86.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Alexander Kensert a****t@g****m 86

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 146 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 10
  • Total maintainers: 1
pypi.org: molcraft

Graph Neural Networks for Molecular Machine Learning

  • Homepage: https://github.com/compomics/molcraft
  • Documentation: https://molcraft.readthedocs.io/
  • 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.
  • Latest release: 0.1.0a11
    published 6 months ago
  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 146 Last month
Rankings
Dependent packages count: 9.3%
Average: 30.9%
Dependent repos count: 52.5%
Maintainers (1)
akensert
Last synced: 6 months ago

Dependencies

docs/requirements.txt pypi
  • ipython *
  • pandas *
  • rdkit *
  • sphinx ==8.2.3
  • sphinx_rtd_theme >=1.0
  • tensorflow *
pyproject.toml pypi