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2025
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| Online Access: | https://doi.org/10.5281/zenodo.17553385 |
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| _version_ | 1866901743848128512 |
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| author | Sneha Sil |
| author_facet | Sneha Sil |
| contents | <p>Source code is attached in this release if you would like to edit the code to suit your particular cheminformatics pipeline. Functions can be commented out/adjusted in functions.py, classes.py is where the graph elements are constructed as dictionaries, and graph.py is where it all comes together.</p> <p>The datasets_regression_tar_files.zip folder contains:</p> <ul> <li>.csv files for all of the regression-type datasets used in our study</li> <li>.tar files containing Cartesian coordinates/Shermo output/JANPA output/NBO output files for each molecule in each dataset (which can be unpacked and loaded into a database using graphpancake)</li> </ul> <p>The datasets_classification_tar_files.zip folder contains:</p> <ul> <li>.csv files for all of the classification-type datasets used in our study</li> <li>.tar files containing Cartesian coordinates/Shermo output/JANPA output/NBO output files for each molecule in each dataset (which can be unpacked and loaded into a database using graphpancake)</li> </ul> <p>The datasets_db_files.zip folder contains:</p> <ul> <li>.db files corresponding to aforementioned unpacking/loading with graphpancake for all (classification and regression) datasets used in our study</li> </ul> <p>The ML_scripts.zip folder contains:</p> <ul> <li>csvs: our datasets, with data and labels</li> <li>data_files: .db files</li> <li>scripts: our machine learning (RF, message passing neural networks) scripts</li> </ul> <p>Note: this is a re-release to receive a DOI from Zenodo.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17553385 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | sneha-sil/graphpancake: datasets and ML scripts Sneha Sil <p>Source code is attached in this release if you would like to edit the code to suit your particular cheminformatics pipeline. Functions can be commented out/adjusted in functions.py, classes.py is where the graph elements are constructed as dictionaries, and graph.py is where it all comes together.</p> <p>The datasets_regression_tar_files.zip folder contains:</p> <ul> <li>.csv files for all of the regression-type datasets used in our study</li> <li>.tar files containing Cartesian coordinates/Shermo output/JANPA output/NBO output files for each molecule in each dataset (which can be unpacked and loaded into a database using graphpancake)</li> </ul> <p>The datasets_classification_tar_files.zip folder contains:</p> <ul> <li>.csv files for all of the classification-type datasets used in our study</li> <li>.tar files containing Cartesian coordinates/Shermo output/JANPA output/NBO output files for each molecule in each dataset (which can be unpacked and loaded into a database using graphpancake)</li> </ul> <p>The datasets_db_files.zip folder contains:</p> <ul> <li>.db files corresponding to aforementioned unpacking/loading with graphpancake for all (classification and regression) datasets used in our study</li> </ul> <p>The ML_scripts.zip folder contains:</p> <ul> <li>csvs: our datasets, with data and labels</li> <li>data_files: .db files</li> <li>scripts: our machine learning (RF, message passing neural networks) scripts</li> </ul> <p>Note: this is a re-release to receive a DOI from Zenodo.</p> |
| title | sneha-sil/graphpancake: datasets and ML scripts |
| url | https://doi.org/10.5281/zenodo.17553385 |