Table 1 Predictive performance results of MoleculeFormer on 10 commonly used public datasets

Dataset	Split type	Metric	MoleculeNet (Graph)	Chemprop (optimized)	Attentive FP	HRGCN+	XGBoost	FP-GNN	MoleculeFormer
BACE	Random	ROC-AUC		0.898	0.876	0.891	0.889	0.881	0.913
	Scaffold	ROC-AUC	0.806	0.857				0.860	0.874
HIV	Random	ROC-AUC		0.827	0.822	0.824	0.816	0.825	0.837
	Scaffold	ROC-AUC	0.763	0.794				0.824	0.830
BBBP	Random	ROC-AUC			0.887	0.926	0.926	0.935	0.932
	Scaffold	ROC-AUC	0.690	0.886				0.916	0.924
Tox21	Random	ROC-AUC	0.832	0.897	0.852	0.848	0.836	0.815	0.839
ClinTox	Random	ROC-AUC	0.832	0.897	0.904	0.899	0.911	0.840	0.883
SIDER	Random	ROC-AUC	0.638	0.658	0.623	0.641	0.642	0.661	0.707
MUV	Random	PRC-AUC	0.109	0.053	0.038	0.082	0.068	0.09	0.144
FreeSolv	Random	RMSE	1.150	1.009	1.091	0.926	1.025	0.905	1.022
ESOL	Random	RMSE	0.580	0.587	0.587	0.563	0.582	0.675	0.645
Lipophileicity	Random	RMSE	0.655	0.563	0.553	0.603	0.574	0.625	0.649

Each dataset is split into training, validation, and test sets with a ratio of 8:1:1. The bold font indicates the top model with the highest scores. Attentive FP and HRGCN+ results are from Wu et al.²⁶. FP-GNN results are from Cai et al.²⁷.

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