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### Performances

Index splitting

|Dataset    |N(tasks)	|N(samples) |Model               |Train score/ROC-AUC|Valid score/ROC-AUC|Time(loading)/s |Time(running)/s|

|-----------|-----------|-----------|--------------------|-------------------|-------------------|----------------|---------------| 

|tox21      |12         |8014       |logistic regression |0.910              |0.759              |30              |60             |

|           |           |           |tensorflow(MT-NN)   |0.987              |0.800              |30              |60             |

|           |           |           |robust MT-NN        |0.979              |0.741              |30              |90             |

|           |           |           |graph convolution   |0.930              |0.819              |40              |160            |

|muv        |17         |93127      |logistic regression |0.910              |0.744              |600             |450            |

|           |           |           |tensorflow(MT-NN)   |0.980              |0.710              |600             |400            |

|           |           |           |robust MT-NN        |0.986              |0.672              |600             |550            |

|           |           |           |graph convolution   |0.881              |0.832              |800             |1800           |

|pcba       |128        |439863     |logistic regression |0.794        	     |0.762              |1800            |10000          |

|           |           |           |tensorflow(MT-NN)	 |0.949        	     |0.791              |1800            |9000           |

|           |           |           |graph convolution   |0.866        	     |0.836              |2200            |14000          |

|sider      |27         |1427       |logistic regression |0.900        	     |0.620              |15              |80             |

|           |           |           |tensorflow(MT-NN)	 |0.931        	     |0.647              |15              |75             |

|           |           |           |graph convolution   |0.845        	     |0.646              |20              |50             |

|toxcast    |617        |8615       |logistic regression |0.762        	     |0.622              |80              |2600           |

|           |           |           |tensorflow(MT-NN)	 |0.926        	     |0.705              |80              |2300           |

|           |           |           |graph convolution   |0.906        	     |0.725              |80              |900            |

Random splitting(N(tasks), N(samples), Time omitted)

|Dataset    |Model               |Train score/ROC-AUC|Valid score/ROC-AUC|Time(loading)/s |Time(running)/s|

|-----------|--------------------|-------------------|-------------------|----------------|---------------| 

|tox21      |logistic regression |0.910              |0.759              |30              |60             |

|           |tensorflow(MT-NN)   |0.987              |0.800              |30              |60             |

|           |robust MT-NN        |0.979              |0.741              |30              |90             |

|           |graph convolution   |0.930              |0.819              |40              |160            |

|muv        |logistic regression |0.910              |0.744              |600             |450            |

|           |tensorflow(MT-NN)   |0.980              |0.710              |600             |400            |

|           |robust MT-NN        |0.986              |0.672              |600             |550            |

|           |graph convolution   |0.881              |0.832              |800             |1800           |

|pcba       |logistic regression |0.794        	     |0.762              |1800            |10000          |

|           |tensorflow(MT-NN)	|0.949        	     |0.791              |1800            |9000           |

|           |graph convolution   |0.866        	     |0.836              |2200            |14000          |

|sider      |logistic regression |0.900        	     |0.620              |15              |80             |

|           |tensorflow(MT-NN)	|0.931        	     |0.647              |15              |75             |

|           |graph convolution   |0.845        	     |0.646              |20              |50             |

|toxcast    |logistic regression |0.762        	     |0.622              |80              |2600           |

|           |tensorflow(MT-NN)	|0.926        	     |0.705              |80              |2300           |

|           |graph convolution   |0.906        	     |0.725              |80              |900            |

Random splitting(Time omitted)

|Dataset    |Model               |Train score/ROC-AUC|Valid score/ROC-AUC|

|-----------|--------------------|-------------------|-------------------|

|           |robust MT-NN        |0.814              |0.692              |

|           |graph convolution   |0.820        	     |0.692              |

Scaffold splitting(N(tasks), N(samples), Time omitted)

Scaffold splitting(Time omitted)

|Dataset    |Model               |Train score/ROC-AUC|Valid score/ROC-AUC|

|-----------|--------------------|-------------------|-------------------|

|           |robust MT-NN        |0.830              |0.614              |

|           |graph convolution   |0.832              |0.638              |

Number of tasks and examples in the datasets

|Dataset    |N(tasks)	|N(samples) |

|-----------|-----------|-----------| 

|tox21      |12         |8014       |

|muv        |17         |93127      |

|pcba       |128        |439863     |

|sider      |27         |1427       |

|toxcast    |617        |8615       |

## Contributing to DeepChem

We actively encourage community contributions to DeepChem. The first place to start getting involved is by running our examples locally. Afterwards, we encourage contributors to give a shot to improving our documentation. While we take effort to provide good docs, there's plenty of room for improvement. All docs are hosted on Github, either in this `README.md` file, or in the `docs/` directory.