gistfile1.txt

import numpy as np
import deepchem as dc

datasets = ['muv']
models = ['weave']
metrics = [dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean),dc.metrics.Metric(dc.metrics.prc_auc_score, np.mean)]

for model in models:
    print("RUNNING:",model)
    dc.molnet.run_benchmark(datasets,model,test=True,metric=metrics)


datasets = ['qm9']
models = ['graphconvreg']

metrics = [
    dc.metrics.Metric(dc.metrics.rms_score, np.mean),
    dc.metrics.Metric(dc.metrics.mean_squared_error, np.mean),
    dc.metrics.Metric(dc.metrics.mean_absolute_error, np.mean),
    dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean),
    dc.metrics.Metric(dc.metrics.r2_score, np.mean)
]

for model in models:
    print("RUNNING:",model)
    dc.molnet.run_benchmark(datasets,model,test=True,metric=metrics)

QM9

gcnn

	MoleculeNet MTL, run_benchmark	Regenerated MTL, run_benchmark	Regenerated MTL, examples
mu	0.583	0.590	1.038
alpha	1.370	2.532	4.611
homo	0.007	0.005	0.015
lumo	0.009	0.006	0.031
gap	0.001	0.008	0.033
r2	35.900	104.927	153.584
zpve	0.003	0.006	0.023
cv	3.410	1.298	3.047
u0	3.410	11.502	25.658
u298	3.410	11.514	25.660
h298	3.410	11.511	25.658
g298	0.650	11.517	25.663

mpnn

	MoleculeNet MTL, run_benchmark	Regenerated MTL, run_benchmark	Regenerated MTL, examples
mu	0.358	0.177	1.158
alpha	0.890	0.444	1.441
homo	0.005	0.004	0.044
lumo	0.006	0.004	0.061
gap	0.008	0.005	0.069
r2	28.500	12.988	8.053
zpve	0.002	0.001	0.029
cv	2.050	0.232	1.068
u0	2.000	0.927	1.655
u298	2.020	0.928	1.698
h298	2.020	0.928	1.674
g298	0.420	0.928	1.670

dtnn

	MoleculeNet MTL, run_benchmark	Regenerated MTL, run_benchmark	Regenerated MTL, examples
mu	0.024	0.212	--
alpha	0.950	0.485	--
homo	0.004	0.004	--
lumo	0.005	0.004	--
gap	0.007	0.005	--
r2	17.000	9.369	--
zpve	0.002	0.001	--
cv	2.430	0.162	--
u0	2.430	0.916	--
u298	2.430	0.916	--
h298	2.430	0.916	--
g298	0.270	0.916	--

MUV

gcnn

	MoleculeNet MTL, run_benchmark	Regenerated MTL, run_benchmark	Regenerated MTL, examples
MUV-466	--	0.005	0.008
MUV-548	--	0.062	0.002
MUV-600	--	0.010	0.005
MUV-644	--	0.011	0.009
MUV-652	--	0.001	0.074
MUV-689	--	0.014	0.002
MUV-692	--	0.001	0.006
MUV-712	--	0.001	0.065
MUV-713	--	0.064	0.008
MUV-733	--	0.012	0.004
MUV-737	--	0.005	0.027
MUV-810	--	0.008	0.006
MUV-832	--	0.003	0.027
MUV-846	--	0.167	0.024
MUV-852	--	0.056	0.060
MUV-858	--	0.009	0.006
MUV-859	--	0.004	0.002
average	0.046	0.026	0.020

weave

	MoleculeNet MTL, run_benchmark	Regenerated MTL, run_benchmark	Regenerated MTL, examples
MUV-466	--	0.501	0.000
MUV-548	--	0.500	0.000
MUV-600	--	0.501	0.003
MUV-644	--	0.501	0.003
MUV-652	--	0.501	0.001
MUV-689	--	0.502	0.001
MUV-692	--	0.501	0.006
MUV-712	--	0.500	0.005
MUV-713	--	0.501	0.002
MUV-733	--	0.502	0.004
MUV-737	--	0.501	0.002
MUV-810	--	0.501	0.001
MUV-832	--	0.501	0.003
MUV-846	--	0.500	0.003
MUV-852	--	0.501	0.004
MUV-858	--	0.502	0.002
MUV-859	--	0.501	0.002
average	0.109	0.501	0.003