Previous MoleculeNet Benchmark Score

These are old scores which is related to MoleculeNet database.
I moved them from the deepchem repo. (see : https://github.com/deepchem/deepchem/pull/2339)

## classification
### Index splitting

|Dataset |Model |Train score/ROC-AUC|Valid score/ROC-AUC|
|-----------|--------------------|-------------------|-------------------|
|clintox |Logistic regression |0.969 |0.683 |
| |Random forest |0.995 |0.763 |
| |XGBoost |0.879 |0.890 |
| |IRV |0.762 |0.811 |
| |MT-NN classification|0.929 |0.832 |
| |Robust MT-NN |0.948 |0.840 |
| |Graph convolution |0.961 |0.812 |
| |DAG |0.997 |0.660 |
| |Weave |0.937 |0.887 |
|hiv |Logistic regression |0.861 |0.731 |
| |Random forest |0.999 |0.720 |
| |XGBoost |0.917 |0.745 |
| |IRV |0.841 |0.724 |
| |NN classification |0.712 |0.676 |
| |Robust NN |0.740 |0.699 |
| |Graph convolution |0.888 |0.771 |
| |Weave |0.880 |0.758 |
|muv |Logistic regression |0.957 |0.754 |
| |XGBoost |0.895 |0.714 |
| |MT-NN classification|0.900 |0.746 |
| |Robust MT-NN |0.937 |0.765 |
| |Graph convolution |0.890 |0.804 |
| |Weave |0.749 |0.764 |
|pcba |Logistic regression |0.807 |0.773 |
| |XGBoost |0.931 |0.847 |
| |MT-NN classification|0.819 |0.792 |
| |Robust MT-NN |0.812 |0.782 |
| |Graph convolution |0.886 |0.851 |
|sider |Logistic regression |0.932 |0.622 |
| |Random forest |1.000 |0.669 |
| |XGBoost |0.829 |0.639 |
| |IRV |0.649 |0.643 |
| |MT-NN classification|0.781 |0.630 |
| |Robust MT-NN |0.805 |0.634 |
| |Graph convolution |0.744 |0.593 |
| |DAG |0.908 |0.558 |
| |Weave |0.622 |0.599 |
|tox21 |Logistic regression |0.902 |0.705 |
| |Random forest |0.999 |0.736 |
| |XGBoost |0.891 |0.753 |
| |IRV |0.811 |0.767 |
| |MT-NN classification|0.854 |0.768 |
| |Robust MT-NN |0.857 |0.766 |
| |Graph convolution |0.903 |0.814 |
| |DAG |0.871 |0.733 |
| |Weave |0.844 |0.797 |
|toxcast |Logistic regression |0.724 |0.577 |
| |XGBoost |0.738 |0.621 |
| |IRV |0.662 |0.643 |
| |MT-NN classification|0.830 |0.684 |
| |Robust MT-NN |0.825 |0.681 |
| |Graph convolution |0.849 |0.726 |
| |Weave |0.796 |0.725 |

### Random splitting

|Dataset |Model |Train score/ROC-AUC|Valid score/ROC-AUC|
|-----------|--------------------|-------------------|-------------------|
|bace_c |Logistic regression |0.952 |0.860 |
| |Random forest |1.000 |0.882 |
| |IRV |0.876 |0.871 |
| |NN classification |0.868 |0.838 |
| |Robust NN |0.892 |0.853 |
| |Graph convolution |0.849 |0.793 |
| |DAG |0.873 |0.810 |
| |Weave |0.828 |0.847 |
|bbbp |Logistic regression |0.978 |0.905 |
| |Random forest |1.000 |0.908 |
| |IRV |0.912 |0.889 |
| |NN classification |0.857 |0.822 |
| |Robust NN |0.886 |0.857 |
| |Graph convolution |0.966 |0.870 |
| |DAG |0.986 |0.888 |
| |Weave |0.935 |0.898 |
|clintox |Logistic regression |0.968 |0.734 |
| |Random forest |0.996 |0.730 |
| |XGBoost |0.886 |0.731 |
| |IRV |0.793 |0.751 |
| |MT-NN classification|0.946 |0.793 |
| |Robust MT-NN |0.958 |0.818 |
| |Graph convolution |0.965 |0.908 |
| |DAG |0.998 |0.529 |
| |Weave |0.927 |0.867 |
|hiv |Logistic regression |0.855 |0.816 |
| |Random forest |0.999 |0.850 |
| |XGBoost |0.933 |0.841 |
| |IRV |0.831 |0.836 |
| |NN classification |0.699 |0.695 |
| |Robust NN |0.726 |0.726 |
| |Graph convolution |0.876 |0.824 |
| |Weave |0.872 |0.819 |
|muv |Logistic regression |0.954 |0.722 |
| |XGBoost |0.874 |0.696 |
| |IRV |0.690 |0.630 |
| |MT-NN classification|0.906 |0.737 |
| |Robust MT-NN |0.940 |0.732 |
| |Graph convolution |0.889 |0.734 |
| |Weave |0.757 |0.714 |
|pcba |Logistic regression |0.808 |0.775 |
| |MT-NN classification|0.811 |0.787 |
| |Robust MT-NN |0.809 |0.776 |
| |Graph convolution |0.888 |0.850 |
|sider |Logistic regression |0.931 |0.639 |
| |Random forest |1.000 |0.682 |
| |XGBoost |0.824 |0.635 |
| |IRV |0.636 |0.634 |
| |MT-NN classification|0.782 |0.662 |
| |Robust MT-NN |0.807 |0.661 |
| |Graph convolution |0.732 |0.666 |
| |DAG |0.919 |0.555 |
| |Weave |0.597 |0.610 |
|tox21 |Logistic regression |0.900 |0.735 |
| |Random forest |0.999 |0.763 |
| |XGBoost |0.874 |0.773 |
| |IRV |0.807 |0.770 |
| |MT-NN classification|0.849 |0.754 |
| |Robust MT-NN |0.854 |0.755 |
| |Graph convolution |0.901 |0.832 |
| |DAG |0.888 |0.766 |
| |Weave |0.844 |0.812 |
|toxcast |Logistic regression |0.719 |0.538 |
| |XGBoost |0.738 |0.633 |
| |IRV |0.659 |0.662 |
| |MT-NN classification|0.836 |0.676 |
| |Robust MT-NN |0.828 |0.680 |
| |Graph convolution |0.843 |0.732 |
| |Weave |0.785 |0.718 |

### Scaffold splitting

|Dataset |Model |Train score/ROC-AUC|Valid score/ROC-AUC|
|-----------|--------------------|-------------------|-------------------|
|bace_c |Logistic regression |0.957 |0.726 |
| |Random forest |0.999 |0.728 |
| |IRV |0.899 |0.700 |
| |NN classification |0.884 |0.710 |
| |Robust NN |0.906 |0.738 |
| |Graph convolution |0.921 |0.665 |
| |DAG |0.839 |0.591 |
| |Weave |0.736 |0.593 |
|bbbp |Logistic regression |0.980 |0.957 |
| |Random forest |1.000 |0.955 |
| |IRV |0.914 |0.962 |
| |NN classification |0.884 |0.955 |
| |Robust NN |0.905 |0.959 |
| |Graph convolution |0.972 |0.949 |
| |DAG |0.940 |0.855 |
| |Weave |0.953 |0.969 |
|clintox |Logistic regression |0.962 |0.687 |
| |Random forest |0.994 |0.664 |
| |XGBoost |0.873 |0.850 |
| |IRV |0.793 |0.715 |
| |MT-NN classification|0.923 |0.825 |
| |Robust MT-NN |0.949 |0.821 |
| |Graph convolution |0.973 |0.847 |
| |DAG |0.991 |0.451 |
| |Weave |0.936 |0.930 |
|hiv |Logistic regression |0.858 |0.793 |
| |Random forest |0.946 |0.562 |
| |XGBoost |0.927 |0.830 |
| |IRV |0.847 |0.811 |
| |NN classification |0.719 |0.718 |
| |Robust NN |0.740 |0.730 |
| |Graph convolution |0.882 |0.797 |
| |Weave |0.880 |0.793 |
|muv |Logistic regression |0.950 |0.756 |
| |XGBoost |0.875 |0.705 |
| |IRV |0.666 |0.708 |
| |MT-NN classification|0.908 |0.785 |
| |Robust MT-NN |0.934 |0.792 |
| |Graph convolution |0.899 |0.787 |
| |Weave |0.762 |0.764 |
|pcba |Logistic regression |0.810 |0.748 |
| |MT-NN classification|0.823 |0.773 |
| |Robust MT-NN |0.818 |0.758 |
| |Graph convolution |0.894 |0.826 |
|sider |Logistic regression |0.926 |0.594 |
| |Random forest |1.000 |0.611 |
| |XGBoost |0.796 |0.560 |
| |IRV |0.638 |0.598 |
| |MT-NN classification|0.771 |0.555 |
| |Robust MT-NN |0.795 |0.567 |
| |Graph convolution |0.751 |0.546 |
| |DAG |0.902 |0.541 |
| |Weave |0.640 |0.509 |
|tox21 |Logistic regression |0.901 |0.676 |
| |Random forest |0.999 |0.665 |
| |XGBoost |0.881 |0.703 |
| |IRV |0.823 |0.708 |
| |MT-NN classification|0.863 |0.725 |
| |Robust MT-NN |0.861 |0.724 |
| |Graph convolution |0.913 |0.764 |
| |DAG |0.888 |0.658 |
| |Weave |0.864 |0.763 |
|toxcast |Logistic regression |0.717 |0.511 |
| |XGBoost |0.741 |0.587 |
| |IRV |0.677 |0.612 |
| |MT-NN classification|0.835 |0.612 |
| |Robust MT-NN |0.832 |0.609 |
| |Graph convolution |0.859 |0.646 |
| |Weave |0.802 |0.657 |

## Regression

|Dataset |Model |Splitting |Train score/R2|Valid score/R2|
|----------------|--------------------|------------|--------------|--------------|
|bace_r |Random forest |Random |0.958 |0.680 |
| |NN regression |Random |0.895 |0.732 |
| |Graphconv regression|Random |0.328 |0.276 |
| |DAG regression |Random |0.370 |0.271 |
| |Weave regression |Random |0.555 |0.578 |
| |Random forest |Scaffold |0.956 |0.203 |
| |NN regression |Scaffold |0.894 |0.203 |
| |Graphconv regression|Scaffold |0.321 |0.032 |
| |DAG regression |Scaffold |0.304 |0.000 |
| |Weave regression |Scaffold |0.594 |0.044 |
|chembl |MT-NN regression |Index |0.828 |0.565 |
| |Graphconv regression|Index |0.192 |0.293 |
| |MT-NN regression |Random |0.829 |0.562 |
| |Graphconv regression|Random |0.198 |0.271 |
| |MT-NN regression |Scaffold |0.843 |0.430 |
| |Graphconv regression|Scaffold |0.231 |0.294 |
|clearance |Random forest |Index |0.953 |0.244 |
| |NN regression |Index |0.884 |0.211 |
| |Graphconv regression|Index |0.696 |0.230 |
| |Weave regression |Index |0.261 |0.107 |
| |Random forest |Random |0.952 |0.547 |
| |NN regression |Random |0.880 |0.273 |
| |Graphconv regression|Random |0.685 |0.302 |
| |Weave regression |Random |0.229 |0.129 |
| |Random forest |Scaffold |0.952 |0.266 |
| |NN regression |Scaffold |0.871 |0.154 |
| |Graphconv regression|Scaffold |0.628 |0.277 |
| |Weave regression |Scaffold |0.228 |0.226 |
|delaney |Random forest |Index |0.954 |0.625 |
| |XGBoost |Index |0.898 |0.664 |
| |NN regression |Index |0.869 |0.585 |
| |Graphconv regression|Index |0.969 |0.813 |
| |DAG regression |Index |0.976 |0.850 |
| |Weave regression |Index |0.963 |0.872 |
| |Random forest |Random |0.955 |0.561 |
| |XGBoost |Random |0.927 |0.727 |
| |NN regression |Random |0.875 |0.495 |
| |Graphconv regression|Random |0.976 |0.787 |
| |DAG regression |Random |0.968 |0.899 |
| |Weave regression |Random |0.955 |0.907 |
| |Random forest |Scaffold |0.953 |0.281 |
| |XGBoost |Scaffold |0.890 |0.316 |
| |NN regression |Scaffold |0.872 |0.308 |
| |Graphconv regression|Scaffold |0.980 |0.564 |
| |DAG regression |Scaffold |0.968 |0.676 |
| |Weave regression |Scaffold |0.971 |0.756 |
|hopv |Random forest |Index |0.943 |0.338 |
| |MT-NN regression |Index |0.725 |0.293 |
| |Graphconv regression|Index |0.307 |0.284 |
| |Weave regression |Index |0.046 |0.026 |
| |Random forest |Random |0.943 |0.513 |
| |MT-NN regression |Random |0.716 |0.289 |
| |Graphconv regression|Random |0.329 |0.239 |
| |Weave regression |Random |0.080 |0.084 |
| |Random forest |Scaffold |0.946 |0.470 |
| |MT-NN regression |Scaffold |0.719 |0.429 |
| |Graphconv regression|Scaffold |0.286 |0.155 |
| |Weave regression |Scaffold |0.097 |0.082 |
|kaggle |MT-NN regression |User-defined|0.748 |0.452 |
|lipo |Random forest |Index |0.960 |0.485 |
| |NN regression |Index |0.829 |0.508 |
| |Graphconv regression|Index |0.867 |0.702 |
| |DAG regression |Index |0.957 |0.483 |
| |Weave regression |Index |0.726 |0.607 |
| |Random forest |Random |0.960 |0.514 |
| |NN regression |Random |0.833 |0.476 |
| |Graphconv regression|Random |0.867 |0.631 |
| |DAG regression |Random |0.967 |0.412 |
| |Weave regression |Random |0.747 |0.598 |
| |Random forest |Scaffold |0.959 |0.330 |
| |NN regression |Scaffold |0.830 |0.308 |
| |Graphconv regression|Scaffold |0.875 |0.608 |
| |DAG regression |Scaffold |0.937 |0.368 |
| |Weave regression |Scaffold |0.761 |0.575 |
|nci |XGBoost |Index |0.441 |0.066 |
| |MT-NN regression |Index |0.690 |0.062 |
| |Graphconv regression|Index |0.123 |0.053 |
| |XGBoost |Random |0.409 |0.106 |
| |MT-NN regression |Random |0.698 |0.117 |
| |Graphconv regression|Random |0.117 |0.076 |
| |XGBoost |Scaffold |0.445 |0.046 |
| |MT-NN regression |Scaffold |0.692 |0.036 |
| |Graphconv regression|Scaffold |0.131 |0.036 |
|pdbbind(core) |Random forest |Random |0.921 |0.382 |
| |NN regression |Random |0.764 |0.591 |
| |Graphconv regression|Random |0.774 |0.230 |
| |Random forest(grid) |Random |0.970 |0.401 |
| |NN regression(grid) |Random |0.986 |0.180 |
|pdbbind(refined)|Random forest |Random |0.901 |0.562 |
| |NN regression |Random |0.766 |0.442 |
| |Graphconv regression|Random |0.694 |0.508 |
| |Random forest(grid) |Random |0.963 |0.530 |
| |NN regression(grid) |Random |0.982 |0.484 |
|pdbbind(full) |Random forest |Random |0.879 |0.475 |
| |NN regression |Random |0.311 |0.307 |
| |Graphconv regression|Random |0.183 |0.186 |
| |Random forest(grid) |Random |0.966 |0.524 |
| |NN regression(grid) |Random |0.961 |0.492 |
|ppb |Random forest |Index |0.951 |0.235 |
| |NN regression |Index |0.902 |0.333 |
| |Graphconv regression|Index |0.673 |0.442 |
| |Weave regression |Index |0.418 |0.301 |
| |Random forest |Random |0.950 |0.220 |
| |NN regression |Random |0.903 |0.244 |
| |Graphconv regression|Random |0.646 |0.429 |
| |Weave regression |Random |0.408 |0.284 |
| |Random forest |Scaffold |0.943 |0.176 |
| |NN regression |Scaffold |0.902 |0.144 |
| |Graphconv regression|Scaffold |0.695 |0.391 |
| |Weave regression |Scaffold |0.401 |0.373 |
|qm7 |Random forest |Index |0.942 |0.029 |
| |NN regression |Index |0.782 |0.038 |
| |Graphconv regression|Index |0.982 |0.036 |
| |NN regression(CM) |Index |0.997 |0.989 |
| |DTNN |Index |0.998 |0.997 |
| |Random forest |Random |0.935 |0.429 |
| |NN regression |Random |0.643 |0.554 |
| |Graphconv regression|Random |0.892 |0.740 |
| |NN regression(CM) |Random |0.997 |0.997 |
| |DTNN |Random |0.998 |0.995 |
| |Random forest |Stratified |0.934 |0.430 |
| |NN regression |Stratified |0.630 |0.563 |
| |Graphconv regression|Stratified |0.894 |0.725 |
| |NN regression(CM) |Stratified |0.998 |0.997 |
| |DTNN |Stratified |0.999 |0.998 |
|qm7b |MT-NN regression(CM)|Index |0.900 |0.783 |
| |DTNN |Index |0.926 |0.869 |
| |MT-NN regression(CM)|Random |0.891 |0.849 |
| |DTNN |Random |0.925 |0.902 |
| |MT-NN regression(CM)|Stratified |0.892 |0.862 |
| |DTNN |Stratified |0.922 |0.905 |
|qm8 |Random forest |Index |0.972 |0.616 |
| |MT-NN regression |Index |0.939 |0.604 |
| |Graphconv regression|Index |0.866 |0.704 |
| |MT-NN regression(CM)|Index |0.770 |0.625 |
| |DTNN |Index |0.856 |0.696 |
| |Random forest |Random |0.971 |0.706 |
| |MT-NN regression |Random |0.934 |0.717 |
| |Graphconv regression|Random |0.848 |0.780 |
| |MT-NN regression(CM)|Random |0.753 |0.699 |
| |DTNN |Random |0.842 |0.754 |
| |Random forest |Stratified |0.971 |0.690 |
| |MT-NN regression |Stratified |0.934 |0.712 |
| |Graphconv regression|Stratified |0.846 |0.767 |
| |MT-NN regression(CM)|Stratified |0.761 |0.696 |
| |DTNN |Stratified |0.846 |0.745 |
|qm9 |MT-NN regression |Index |0.839 |0.708 |
| |Graphconv regression|Index |0.754 |0.768 |
| |MT-NN regression(CM)|Index |0.803 |0.800 |
| |DTNN |Index |0.911 |0.867 |
| |MT-NN regression |Random |0.849 |0.753 |
| |Graphconv regression|Random |0.700 |0.696 |
| |MT-NN regression(CM)|Random |0.822 |0.823 |
| |DTNN |Random |0.913 |0.867 |
| |MT-NN regression |Stratified |0.839 |0.687 |
| |Graphconv regression|Stratified |0.724 |0.696 |
| |MT-NN regression(CM)|Stratified |0.791 |0.827 |
| |DTNN |Stratified |0.911 |0.874 |
|sampl |Random forest |Index |0.967 |0.737 |
| |XGBoost |Index |0.884 |0.784 |
| |NN regression |Index |0.923 |0.758 |
| |Graphconv regression|Index |0.970 |0.897 |
| |DAG regression |Index |0.970 |0.871 |
| |Weave regression |Index |0.992 |0.915 |
| |Random forest |Random |0.966 |0.729 |
| |XGBoost |Random |0.906 |0.745 |
| |NN regression |Random |0.931 |0.689 |
| |Graphconv regression|Random |0.964 |0.848 |
| |DAG regression |Random |0.973 |0.861 |
| |Weave regression |Random |0.992 |0.885 |
| |Random forest |Scaffold |0.967 |0.465 |
| |XGBoost |Scaffold |0.918 |0.439 |
| |NN regression |Scaffold |0.901 |0.238 |
| |Graphconv regression|Scaffold |0.963 |0.822 |
| |DAG regression |Scaffold |0.961 |0.846 |
| |Weave regression |Scaffold |0.992 |0.837 |