Table 3.

The test performance on the FreeSolv-PHYSPROP dataset. Each model is trained on 50 random splits and the mean and standard deviation are reported. sPhysNet-MT-ens5 is a deep ensemble of five sPhysNet-MT models. The errors in hydration free energies are reported in kcal/mol and the errors in logP are reported in log unit.

Ref.	Model	Target
		Hydration (FreeSolv)		logP (PHYSPROP)
		MAE	RMSE	MAE	RMSE
	sPhysNet-MT	0.392±0.064	0.663±0.135	0.262±0.006	0.421±0.017
	sPhysNet-MT-ens5	0.359±0.063	0.620±0.140	0.242±0.004	0.393±0.012
Previous hydration free energy prediction models
36	SVM	---	0.852±0.171	---	---
	XGBoost	---	1.025±0.185	---	---
	RF	---	1.143±0.230	---	---
	DNN	---	1.013±0.197	---	---
	GCN	---	1.149±0.262	---	---
	GAT	---	1.304±0.272	---	---
	MPNN	---	1.327±0.279	---	---
	Attentive FP	---	1.091±0.191	---	---
40	3DGCN	0.575±0.053	0.824±0.140	---	---
42	AGBT	0.594±0.090	0.994±0.217	---	---
39	D-MPNN	---	0.998±0.207	---	---
37, 68	weave	---	1.220±0.280	---	---
43	A3D-PNAConv	0.417±0.066	0.719±0.168	---	---
69	FML	0.570	---	---	---
Previous logP prediction models
70	QSPR	---	---	---	0.78
60	GraphCNN	---	---	---	0.56
71	$DN{N}_{taut}$	---	---	---	0.47±0.02
	$DN{N}_{mono}$	---	---	---	0.50±0.02
72	OPERA	---	---	---	0.78