Datasets:
Update README.md
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README.md
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- biology
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- chemistry
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configs:
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- config_name:
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data_files:
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- split: train
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path: bace/train.csv
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- split: test
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path: tox21/test.csv
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- split: val
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path: tox21/valid.csv
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- config_name: bace_new
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data_files:
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- split: test
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path: data2/bace/test.csv
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- split: val
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path: data2/bace/valid.csv
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- config_name: bbbp_new
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data_files:
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- split: test
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path: data2/bbbp/test.csv
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- split: val
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path: data2/bbbp/valid.csv
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- config_name: clintox_new
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data_files:
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- split: test
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path: data2/clintox/test.csv
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- split: val
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path: data2/clintox/valid.csv
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- config_name: hiv_new
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data_files:
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- split: test
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path: data2/hiv/test.csv
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- split: val
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path: data2/hiv/valid.csv
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- config_name: sider_new
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data_files:
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- split: test
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path: data2/sider/test.csv
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- split: val
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path: data2/sider/valid.csv
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- config_name: tox21_new
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data_files:
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- split: test
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path: data2/tox21/test.csv
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- split: val
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path: data2/tox21/valid.csv
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---
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# MoleculeNet Benchmark ([website](https://moleculenet.org/))
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MoleculeNet is a benchmark specially designed for testing machine learning methods of molecular properties. As we aim to facilitate the development of molecular machine learning method, this work curates a number of dataset collections, creates a suite of software that implements many known featurizations and previously proposed algorithms. All methods and datasets are integrated as parts of the open source DeepChem package(MIT license).
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MoleculeNet is built upon multiple public databases. The full collection currently includes over 700,000 compounds tested on a range of different properties. We test the performances of various machine learning models with different featurizations on the datasets(detailed descriptions here), with all results reported in AUC-ROC, AUC-PRC, RMSE and MAE scores.
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For users, please cite:
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Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, Vijay Pande, MoleculeNet: A Benchmark for Molecular Machine Learning, arXiv preprint, arXiv: 1703.00564, 2017.
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- biology
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- chemistry
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configs:
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- config_name: bace_new
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data_files:
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- split: test
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path: data2/bace/test.csv
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- split: val
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path: data2/bace/valid.csv
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- config_name: bbbp_new
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data_files:
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- split: test
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path: data2/bbbp/test.csv
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- split: val
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path: data2/bbbp/valid.csv
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- config_name: clintox_new
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data_files:
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- split: test
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path: data2/clintox/test.csv
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- split: val
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path: data2/clintox/valid.csv
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- config_name: hiv_new
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data_files:
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- split: test
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path: data2/hiv/test.csv
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- split: val
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path: data2/hiv/valid.csv
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- config_name: sider_new
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data_files:
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- split: test
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path: data2/sider/test.csv
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- split: val
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path: data2/sider/valid.csv
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- config_name: tox21_new
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data_files:
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- split: test
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path: data2/tox21/test.csv
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- split: val
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path: data2/tox21/valid.csv
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---
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# MoleculeNet Benchmark ([website](https://moleculenet.org/))
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MoleculeNet is a benchmark specially designed for testing machine learning methods of molecular properties. As we aim to facilitate the development of molecular machine learning method, this work curates a number of dataset collections, creates a suite of software that implements many known featurizations and previously proposed algorithms. All methods and datasets are integrated as parts of the open source DeepChem package(MIT license).
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MoleculeNet is built upon multiple public databases. The full collection currently includes over 700,000 compounds tested on a range of different properties. We test the performances of various machine learning models with different featurizations on the datasets(detailed descriptions here), with all results reported in AUC-ROC, AUC-PRC, RMSE and MAE scores.
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For users, please cite:
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Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, Vijay Pande, MoleculeNet: A Benchmark for Molecular Machine Learning, arXiv preprint, arXiv: 1703.00564, 2017.
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<!-- - config_name: bace
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data_files:
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- split: train
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path: bace/train.csv
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- split: test
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path: tox21/test.csv
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- split: val
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path: tox21/valid.csv -->
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