Loading README.md +42 −15 Original line number Diff line number Diff line # DeepChem # DeepChem DeepChem aims to provide a high quality open-source toolchain that democratizes the use of deep-learning in drug discovery, materials science, and quantum Loading Loading @@ -190,6 +190,8 @@ Index splitting |Dataset |Model |Train score/ROC-AUC|Valid score/ROC-AUC| |-----------|--------------------|-------------------|-------------------| |tox21 |logistic regression |0.903 |0.705 | | |Random Forest |0.999 |0.733 | | |IRV |0.811 |0.767 | | |Multitask network |0.856 |0.763 | | |robust MT-NN |0.857 |0.767 | | |graph convolution |0.872 |0.798 | Loading @@ -202,6 +204,8 @@ Index splitting | |robust MT-NN |0.809 |0.783 | | |graph convolution |0.876 |0.852 | |sider |logistic regression |0.933 |0.620 | | |Random Forest |0.999 |0.670 | | |IRV |0.649 |0.642 | | |Multitask network |0.775 |0.634 | | |robust MT-NN |0.803 |0.632 | | |graph convolution |0.708 |0.594 | Loading @@ -210,6 +214,8 @@ Index splitting | |robust MT-NN |0.825 |0.680 | | |graph convolution |0.821 |0.720 | |clintox |logistic regression |0.967 |0.676 | | |Random Forest |0.995 |0.776 | | |IRV |0.763 |0.814 | | |Multitask network |0.934 |0.830 | | |robust MT-NN |0.949 |0.827 | | |graph convolution |0.946 |0.860 | Loading @@ -219,6 +225,8 @@ Random splitting |Dataset |Model |Train score/ROC-AUC|Valid score/ROC-AUC| |-----------|--------------------|-------------------|-------------------| |tox21 |logistic regression |0.902 |0.715 | | |Random Forest |0.999 |0.764 | | |IRV |0.808 |0.767 | | |Multitask network |0.844 |0.795 | | |robust MT-NN |0.855 |0.773 | | |graph convolution |0.865 |0.827 | Loading @@ -231,6 +239,8 @@ Random splitting | |robust MT-NN |0.811 |0.771 | | |graph convolution |0.872 |0.844 | |sider |logistic regression |0.929 |0.656 | | |Random Forest |0.999 |0.665 | | |IRV |0.648 |0.596 | | |Multitask network |0.777 |0.655 | | |robust MT-NN |0.804 |0.630 | | |graph convolution |0.705 |0.618 | Loading @@ -239,6 +249,8 @@ Random splitting | |robust MT-NN |0.822 |0.681 | | |graph convolution |0.820 |0.717 | |clintox |logistic regression |0.972 |0.725 | | |Random Forest |0.997 |0.670 | | |IRV |0.809 |0.846 | | |Multitask network |0.951 |0.834 | | |robust MT-NN |0.959 |0.830 | | |graph convolution |0.975 |0.876 | Loading @@ -248,6 +260,8 @@ Scaffold splitting |Dataset |Model |Train score/ROC-AUC|Valid score/ROC-AUC| |-----------|--------------------|-------------------|-------------------| |tox21 |logistic regression |0.900 |0.650 | | |Random Forest |0.999 |0.629 | | |IRV |0.823 |0.708 | | |Multitask network |0.863 |0.703 | | |robust MT-NN |0.861 |0.710 | | |graph convolution |0.885 |0.732 | Loading @@ -260,6 +274,8 @@ Scaffold splitting | |robust MT-NN |0.812 |0.756 | | |graph convolution |0.874 |0.817 | |sider |logistic regression |0.926 |0.592 | | |Random Forest |0.999 |0.619 | | |IRV |0.639 |0.599 | | |Multitask network |0.776 |0.557 | | |robust MT-NN |0.797 |0.560 | | |graph convolution |0.722 |0.583 | Loading @@ -268,6 +284,8 @@ Scaffold splitting | |robust MT-NN |0.830 |0.614 | | |graph convolution |0.832 |0.638 | |clintox |logistic regression |0.960 |0.803 | | |Random Forest |0.993 |0.735 | | |IRV |0.793 |0.718 | | |Multitask network |0.947 |0.862 | | |robust MT-NN |0.953 |0.890 | | |graph convolution |0.957 |0.823 | Loading @@ -276,38 +294,47 @@ Scaffold splitting |Dataset |Model |Splitting |Train score/R2|Valid score/R2| |----------------|--------------------|------------|--------------|--------------| |delaney |MT-NN regression |Index |0.868 |0.578 | |delaney |Random Forest |Index |0.953 |0.626 | | |NN regression |Index |0.868 |0.578 | | |graphconv regression|Index |0.967 |0.790 | | |MT-NN regression |Random |0.865 |0.574 | | |Random Forest |Random |0.951 |0.684 | | |NN regression |Random |0.865 |0.574 | | |graphconv regression|Random |0.964 |0.782 | | |MT-NN regression |Scaffold |0.866 |0.342 | | |Random Forest |Scaffold |0.953 |0.284 | | |NN regression |Scaffold |0.866 |0.342 | | |graphconv regression|Scaffold |0.967 |0.606 | |sampl |MT-NN regression |Index |0.917 |0.764 | |sampl |Random Forest |Index |0.968 |0.736 | | |NN regression |Index |0.917 |0.764 | | |graphconv regression|Index |0.982 |0.864 | | |MT-NN regression |Random |0.908 |0.830 | | |Random Forest |Random |0.967 |0.752 | | |NN regression |Random |0.908 |0.830 | | |graphconv regression|Random |0.987 |0.868 | | |MT-NN regression |Scaffold |0.891 |0.217 | | |Random Forest |Scaffold |0.966 |0.473 | | |NN regression |Scaffold |0.891 |0.217 | | |graphconv regression|Scaffold |0.985 |0.666 | |nci |MT-NN regression |Index |0.171 |0.062 | |nci |NN regression |Index |0.171 |0.062 | | |graphconv regression|Index |0.123 |0.048 | | |MT-NN regression |Random |0.168 |0.085 | | |NN regression |Random |0.168 |0.085 | | |graphconv regression|Random |0.117 |0.076 | | |MT-NN regression |Scaffold |0.180 |0.052 | | |NN regression |Scaffold |0.180 |0.052 | | |graphconv regression|Scaffold |0.131 |0.046 | |pdbbind(core) |MT-NN regression |Random |0.973 |0.494 | |pdbbind(refined)|MT-NN regression |Random |0.987 |0.503 | |pdbbind(full) |MT-NN regression |Random |0.983 |0.528 | |pdbbind(core) |Random Forest |Random |0.969 |0.445 | | |NN regression |Random |0.973 |0.494 | |pdbbind(refined)|Random Forest |Random |0.963 |0.511 | | |NN regression |Random |0.987 |0.503 | |pdbbind(full) |Random Forest |Random |0.965 |0.493 | | |NN regression |Random |0.983 |0.528 | |chembl |MT-NN regression |Index |0.443 |0.427 | | |MT-NN regression |Random |0.464 |0.434 | | |MT-NN regression |Scaffold |0.484 |0.361 | |gdb7 |MT-NN regression |Index |0.994 |0.010 | |qm7 |MT-NN regression |Index |0.994 |0.010 | | |MT-NN regression |Random |0.860 |0.773 | | |MT-NN regression |User-defined|0.996 |0.996 | |kaggle |MT-NN regression |User-defined|0.748 |0.452 | |Dataset |Model |Splitting |Train score/MAE(kcal/mol)|Valid score/MAE(kcal/mol)| |----------------|--------------------|------------|-------------------------|-------------------------| |gdb7 |MT-NN regression |Index |18.3 |172.0 | |qm7 |MT-NN regression |Index |18.3 |172.0 | | |MT-NN regression |Random |44.3 |59.1 | | |MT-NN regression |User-defined|9.0 |9.5 | Loading Loading
README.md +42 −15 Original line number Diff line number Diff line # DeepChem # DeepChem DeepChem aims to provide a high quality open-source toolchain that democratizes the use of deep-learning in drug discovery, materials science, and quantum Loading Loading @@ -190,6 +190,8 @@ Index splitting |Dataset |Model |Train score/ROC-AUC|Valid score/ROC-AUC| |-----------|--------------------|-------------------|-------------------| |tox21 |logistic regression |0.903 |0.705 | | |Random Forest |0.999 |0.733 | | |IRV |0.811 |0.767 | | |Multitask network |0.856 |0.763 | | |robust MT-NN |0.857 |0.767 | | |graph convolution |0.872 |0.798 | Loading @@ -202,6 +204,8 @@ Index splitting | |robust MT-NN |0.809 |0.783 | | |graph convolution |0.876 |0.852 | |sider |logistic regression |0.933 |0.620 | | |Random Forest |0.999 |0.670 | | |IRV |0.649 |0.642 | | |Multitask network |0.775 |0.634 | | |robust MT-NN |0.803 |0.632 | | |graph convolution |0.708 |0.594 | Loading @@ -210,6 +214,8 @@ Index splitting | |robust MT-NN |0.825 |0.680 | | |graph convolution |0.821 |0.720 | |clintox |logistic regression |0.967 |0.676 | | |Random Forest |0.995 |0.776 | | |IRV |0.763 |0.814 | | |Multitask network |0.934 |0.830 | | |robust MT-NN |0.949 |0.827 | | |graph convolution |0.946 |0.860 | Loading @@ -219,6 +225,8 @@ Random splitting |Dataset |Model |Train score/ROC-AUC|Valid score/ROC-AUC| |-----------|--------------------|-------------------|-------------------| |tox21 |logistic regression |0.902 |0.715 | | |Random Forest |0.999 |0.764 | | |IRV |0.808 |0.767 | | |Multitask network |0.844 |0.795 | | |robust MT-NN |0.855 |0.773 | | |graph convolution |0.865 |0.827 | Loading @@ -231,6 +239,8 @@ Random splitting | |robust MT-NN |0.811 |0.771 | | |graph convolution |0.872 |0.844 | |sider |logistic regression |0.929 |0.656 | | |Random Forest |0.999 |0.665 | | |IRV |0.648 |0.596 | | |Multitask network |0.777 |0.655 | | |robust MT-NN |0.804 |0.630 | | |graph convolution |0.705 |0.618 | Loading @@ -239,6 +249,8 @@ Random splitting | |robust MT-NN |0.822 |0.681 | | |graph convolution |0.820 |0.717 | |clintox |logistic regression |0.972 |0.725 | | |Random Forest |0.997 |0.670 | | |IRV |0.809 |0.846 | | |Multitask network |0.951 |0.834 | | |robust MT-NN |0.959 |0.830 | | |graph convolution |0.975 |0.876 | Loading @@ -248,6 +260,8 @@ Scaffold splitting |Dataset |Model |Train score/ROC-AUC|Valid score/ROC-AUC| |-----------|--------------------|-------------------|-------------------| |tox21 |logistic regression |0.900 |0.650 | | |Random Forest |0.999 |0.629 | | |IRV |0.823 |0.708 | | |Multitask network |0.863 |0.703 | | |robust MT-NN |0.861 |0.710 | | |graph convolution |0.885 |0.732 | Loading @@ -260,6 +274,8 @@ Scaffold splitting | |robust MT-NN |0.812 |0.756 | | |graph convolution |0.874 |0.817 | |sider |logistic regression |0.926 |0.592 | | |Random Forest |0.999 |0.619 | | |IRV |0.639 |0.599 | | |Multitask network |0.776 |0.557 | | |robust MT-NN |0.797 |0.560 | | |graph convolution |0.722 |0.583 | Loading @@ -268,6 +284,8 @@ Scaffold splitting | |robust MT-NN |0.830 |0.614 | | |graph convolution |0.832 |0.638 | |clintox |logistic regression |0.960 |0.803 | | |Random Forest |0.993 |0.735 | | |IRV |0.793 |0.718 | | |Multitask network |0.947 |0.862 | | |robust MT-NN |0.953 |0.890 | | |graph convolution |0.957 |0.823 | Loading @@ -276,38 +294,47 @@ Scaffold splitting |Dataset |Model |Splitting |Train score/R2|Valid score/R2| |----------------|--------------------|------------|--------------|--------------| |delaney |MT-NN regression |Index |0.868 |0.578 | |delaney |Random Forest |Index |0.953 |0.626 | | |NN regression |Index |0.868 |0.578 | | |graphconv regression|Index |0.967 |0.790 | | |MT-NN regression |Random |0.865 |0.574 | | |Random Forest |Random |0.951 |0.684 | | |NN regression |Random |0.865 |0.574 | | |graphconv regression|Random |0.964 |0.782 | | |MT-NN regression |Scaffold |0.866 |0.342 | | |Random Forest |Scaffold |0.953 |0.284 | | |NN regression |Scaffold |0.866 |0.342 | | |graphconv regression|Scaffold |0.967 |0.606 | |sampl |MT-NN regression |Index |0.917 |0.764 | |sampl |Random Forest |Index |0.968 |0.736 | | |NN regression |Index |0.917 |0.764 | | |graphconv regression|Index |0.982 |0.864 | | |MT-NN regression |Random |0.908 |0.830 | | |Random Forest |Random |0.967 |0.752 | | |NN regression |Random |0.908 |0.830 | | |graphconv regression|Random |0.987 |0.868 | | |MT-NN regression |Scaffold |0.891 |0.217 | | |Random Forest |Scaffold |0.966 |0.473 | | |NN regression |Scaffold |0.891 |0.217 | | |graphconv regression|Scaffold |0.985 |0.666 | |nci |MT-NN regression |Index |0.171 |0.062 | |nci |NN regression |Index |0.171 |0.062 | | |graphconv regression|Index |0.123 |0.048 | | |MT-NN regression |Random |0.168 |0.085 | | |NN regression |Random |0.168 |0.085 | | |graphconv regression|Random |0.117 |0.076 | | |MT-NN regression |Scaffold |0.180 |0.052 | | |NN regression |Scaffold |0.180 |0.052 | | |graphconv regression|Scaffold |0.131 |0.046 | |pdbbind(core) |MT-NN regression |Random |0.973 |0.494 | |pdbbind(refined)|MT-NN regression |Random |0.987 |0.503 | |pdbbind(full) |MT-NN regression |Random |0.983 |0.528 | |pdbbind(core) |Random Forest |Random |0.969 |0.445 | | |NN regression |Random |0.973 |0.494 | |pdbbind(refined)|Random Forest |Random |0.963 |0.511 | | |NN regression |Random |0.987 |0.503 | |pdbbind(full) |Random Forest |Random |0.965 |0.493 | | |NN regression |Random |0.983 |0.528 | |chembl |MT-NN regression |Index |0.443 |0.427 | | |MT-NN regression |Random |0.464 |0.434 | | |MT-NN regression |Scaffold |0.484 |0.361 | |gdb7 |MT-NN regression |Index |0.994 |0.010 | |qm7 |MT-NN regression |Index |0.994 |0.010 | | |MT-NN regression |Random |0.860 |0.773 | | |MT-NN regression |User-defined|0.996 |0.996 | |kaggle |MT-NN regression |User-defined|0.748 |0.452 | |Dataset |Model |Splitting |Train score/MAE(kcal/mol)|Valid score/MAE(kcal/mol)| |----------------|--------------------|------------|-------------------------|-------------------------| |gdb7 |MT-NN regression |Index |18.3 |172.0 | |qm7 |MT-NN regression |Index |18.3 |172.0 | | |MT-NN regression |Random |44.3 |59.1 | | |MT-NN regression |User-defined|9.0 |9.5 | Loading
