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deepchem / moleculenet

Moleculenet.ai Datasets And Splits
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Previous MoleculeNet Benchmark Score #18

Open nissy-dev opened 3 years ago

nissy-dev commented 3 years ago

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