Loading README.md +15 −1 Original line number Diff line number Diff line Loading @@ -260,8 +260,18 @@ Scaffold splitting |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 | |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 |0.961 |0.011 | | |MT-NN regression |Random |0.742 |0.732 | |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 |44.5 |185.6 | | |MT-NN regression |Random |86.1 |92.2 | * General features Number of tasks and examples in the datasets Loading @@ -279,6 +289,8 @@ Number of tasks and examples in the datasets |pdbbind(core) |1 |195 | |pdbbind(refined)|1 |3706 | |pdbbind(full) |1 |11908 | |chembl(5thresh) |691 |23871 | |gdb7 |1 |7165 | Loading Loading @@ -313,6 +325,8 @@ Time needed for benchmark test(~20h in total) |pdbbind(core) |MT-NN regression |0(featurized) |30 | |pdbbind(refined)|MT-NN regression |0(featurized) |40 | |pdbbind(full) |MT-NN regression |0(featurized) |60 | |chembl |MT-NN regression |200 |9000 | |gdb7 |MT-NN regression |10 |110 | |kaggle |MT-NN regression |2200 |3200 | Loading examples/gdb7/gdb7_sklearn.py +1 −1 Original line number Diff line number Diff line Loading @@ -50,7 +50,7 @@ def model_builder(model_dir): model = dc.models.SingletaskToMultitask(tasks, model_builder, model_dir) # Fit trained model model.fit(train_dataset, nb_epoch=30) model.fit(train_dataset) model.save() train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset, transformers) Loading Loading
README.md +15 −1 Original line number Diff line number Diff line Loading @@ -260,8 +260,18 @@ Scaffold splitting |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 | |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 |0.961 |0.011 | | |MT-NN regression |Random |0.742 |0.732 | |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 |44.5 |185.6 | | |MT-NN regression |Random |86.1 |92.2 | * General features Number of tasks and examples in the datasets Loading @@ -279,6 +289,8 @@ Number of tasks and examples in the datasets |pdbbind(core) |1 |195 | |pdbbind(refined)|1 |3706 | |pdbbind(full) |1 |11908 | |chembl(5thresh) |691 |23871 | |gdb7 |1 |7165 | Loading Loading @@ -313,6 +325,8 @@ Time needed for benchmark test(~20h in total) |pdbbind(core) |MT-NN regression |0(featurized) |30 | |pdbbind(refined)|MT-NN regression |0(featurized) |40 | |pdbbind(full) |MT-NN regression |0(featurized) |60 | |chembl |MT-NN regression |200 |9000 | |gdb7 |MT-NN regression |10 |110 | |kaggle |MT-NN regression |2200 |3200 | Loading
examples/gdb7/gdb7_sklearn.py +1 −1 Original line number Diff line number Diff line Loading @@ -50,7 +50,7 @@ def model_builder(model_dir): model = dc.models.SingletaskToMultitask(tasks, model_builder, model_dir) # Fit trained model model.fit(train_dataset, nb_epoch=30) model.fit(train_dataset) model.save() train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset, transformers) Loading
