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| # scikit-mol | ||
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| ## Scikit-Learn classes for molecular vectorization using RDKit | ||
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| The intended usage is to be able to add molecular vectorization directly into scikit-learn pipelines, so that the final model directly predict on RDKit molecules or SMILES strings | ||
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| As example with the needed scikit-learn and -mol imports and RDKit mol objects in the mol_list_train and _test lists: | ||
| As example with the needed scikit-learn and -mol imports and RDKit mol objects in the mol_list_train and \_test lists: | ||
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| pipe = Pipeline([('mol_transformer', MorganFingerprintTransformer()), ('Regressor', Ridge())]) | ||
| pipe.fit(mol_list_train, y_train) | ||
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@@ -18,39 +20,40 @@ The scikit-learn compatibility should also make it easier to include the fingerp | |
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| The first draft for the project was created at the [RDKIT UGM 2022 hackathon](https://github.com/rdkit/UGM_2022) 2022-October-14 | ||
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| ## Implemented | ||
| * Descriptors | ||
| * MolecularDescriptorTransformer | ||
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| - Descriptors | ||
| - MolecularDescriptorTransformer | ||
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| <br> | ||
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| * Fingerprints | ||
| * MorganFingerprintTransformer | ||
| * MACCSKeysFingerprintTransformer | ||
| * RDKitFingerprintTransformer | ||
| * AtomPairFingerprintTransformer | ||
| * TopologicalTorsionFingerprintTransformer | ||
| * MHFingerprintTransformer | ||
| * SECFingerprintTransformer | ||
| * AvalonFingerprintTransformer | ||
| - Fingerprints | ||
| - MorganFingerprintTransformer | ||
| - MACCSKeysFingerprintTransformer | ||
| - RDKitFingerprintTransformer | ||
| - AtomPairFingerprintTransformer | ||
| - TopologicalTorsionFingerprintTransformer | ||
| - MHFingerprintTransformer | ||
| - SECFingerprintTransformer | ||
| - AvalonFingerprintTransformer | ||
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| <br> | ||
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| * Conversions | ||
| * SmilesToMol | ||
| - Conversions | ||
| - SmilesToMol | ||
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| <br> | ||
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| * Standardizer | ||
| * Standardizer | ||
| - Standardizer | ||
| - Standardizer | ||
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| <br> | ||
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| * Utilities | ||
| * CheckSmilesSanitazion | ||
| - Utilities | ||
| - CheckSmilesSanitazion | ||
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| ## Installation | ||
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| Users can install latest tagged release from pip | ||
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| pip install scikit-mol | ||
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| There are a collection of notebooks in the notebooks directory which demonstrates some different aspects and use cases | ||
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| * [Basic Usage and fingerprint transformers](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/01_basic_usage.ipynb) | ||
| * [Descriptor transformer](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/02_descriptor_transformer.ipynb) | ||
| * [Pipelining with Scikit-Learn classes](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/03_example_pipeline.ipynb) | ||
| * [Molecular standardization](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/04_standardizer.ipynb) | ||
| * [Sanitizing SMILES input](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/05_smiles_sanitaztion.ipynb) | ||
| * [Integrated hyperparameter tuning of Scikit-Learn estimator and Scikit-Mol transformer](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/06_hyperparameter_tuning.ipynb) | ||
| * [Using parallel execution to speed up descriptor and fingerprint calculations](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/07_parallel_transforms.ipynb) | ||
| * [Testing different fingerprints as part of the hyperparameter optimization](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb) | ||
| * [Using pandas output for easy feature importance analysis and combine pre-exisitng values with new computations](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/10_pipeline_pandas_output.ipynb) | ||
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| - [Basic Usage and fingerprint transformers](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/01_basic_usage.ipynb) | ||
| - [Descriptor transformer](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/02_descriptor_transformer.ipynb) | ||
| - [Pipelining with Scikit-Learn classes](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/03_example_pipeline.ipynb) | ||
| - [Molecular standardization](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/04_standardizer.ipynb) | ||
| - [Sanitizing SMILES input](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/05_smiles_sanitaztion.ipynb) | ||
| - [Integrated hyperparameter tuning of Scikit-Learn estimator and Scikit-Mol transformer](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/06_hyperparameter_tuning.ipynb) | ||
| - [Using parallel execution to speed up descriptor and fingerprint calculations](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/07_parallel_transforms.ipynb) | ||
| - [Using skopt for hyperparameter tuning](https://github.com/EBjerrum/scikit-mol/tree/main/notebooks/08_external_library_skopt.ipynb) | ||
| - [Testing different fingerprints as part of the hyperparameter optimization](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb) | ||
| - [Using pandas output for easy feature importance analysis and combine pre-exisitng values with new computations](https://github.com/EBjerrum/scikit-mol/blob/main/notebooks/10_pipeline_pandas_output.ipynb) | ||
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| We also put a software note on ChemRxiv. [https://doi.org/10.26434/chemrxiv-2023-fzqwd](https://doi.org/10.26434/chemrxiv-2023-fzqwd) | ||
| We also put a software note on ChemRxiv. [https://doi.org/10.26434/chemrxiv-2023-fzqwd](https://doi.org/10.26434/chemrxiv-2023-fzqwd) | ||
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| ## Contributing | ||
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| There are more information about how to contribute to the project in [CONTRIBUTION.md](https://github.com/EBjerrum/scikit-mol/CONTRIBUTION.md) | ||
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| ## BUGS | ||
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| Probably still, please check issues at GitHub and report there | ||
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| ## Contributers: | ||
| * Esben Jannik Bjerrum [@ebjerrum](https://github.com/ebjerrum), [email protected] | ||
| * Carmen Esposito [@cespos](https://github.com/cespos) | ||
| * Son Ha, [email protected] | ||
| * Oh-hyeon Choung, [email protected] | ||
| * Andreas Poehlmann, [@ap--](https://github.com/ap--) | ||
| * Ya Chen, [@anya-chen](https://github.com/anya-chen) | ||
| * Rafał Bachorz [@rafalbachorz](https://github.com/rafalbachorz) | ||
| * Adrien Chaton [@adrienchaton](https://github.com/adrienchaton) | ||
| * [@VincentAlexanderScholz](https://github.com/VincentAlexanderScholz) | ||
| * [@RiesBen](https://github.com/RiesBen) | ||
| * [@enricogandini](https://github.com/enricogandini) | ||
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| - Esben Jannik Bjerrum [@ebjerrum](https://github.com/ebjerrum), [email protected] | ||
| - Carmen Esposito [@cespos](https://github.com/cespos) | ||
| - Son Ha, [email protected] | ||
| - Oh-hyeon Choung, [email protected] | ||
| - Andreas Poehlmann, [@ap--](https://github.com/ap--) | ||
| - Ya Chen, [@anya-chen](https://github.com/anya-chen) | ||
| - Rafał Bachorz [@rafalbachorz](https://github.com/rafalbachorz) | ||
| - Adrien Chaton [@adrienchaton](https://github.com/adrienchaton) | ||
| - [@VincentAlexanderScholz](https://github.com/VincentAlexanderScholz) | ||
| - [@RiesBen](https://github.com/RiesBen) | ||
| - [@enricogandini](https://github.com/enricogandini) | ||
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