Table 4.

Results of the proposed WPLMF framework and baselines in SIDER4 (The best performance is highlighted in bold)

Method	AUPR	F1	MMR	Precision@15	Recall@15	Precision	Recall
MCS-MKL	0.5747 (3.6e−3)	0.5624 (2.1e−3)	9.8289 (5.8e−2)	0.6061 (1.1e−2)	0.7741 (2.0e−2)	:0.5693 (1.2e−2)	0.5590 (1.7e−2)
FGRMF	0.5434 (4.4e−3)	0.5347 (3.4e−3)	9.7910 (1.5e−2)	0.7304 (1.0e−2)	0.5416 (6.7e−3)	0.5347 (9.2e−3)	0.5351 (1.1e−2)
idse-HE*	0.5303 (7.7e−3)	0.5069 (2.6e−2)	8.1523 (7.0e−2)	0.5228 (1.9e−2)	0.9026 (1.3e−2)	0.4267 (5.8e−2)	0.6402 (4.5e−2)
Galeano’s	0.5698 (3.6e−3)	0.5457 (7.1e−3)	9.7946 (5.8e−2)	0.7500 (1.3e−2)	0.5567 (2.3e−2)	0.5617 (9.4e−3)	0.5306 (1.3e−2)
Logit MF*	0.5479 (4.9e−3)	0.5402 (4.1e−3)	9.6442 (3.9e−3)	0.7324 (1.3e−2)	0.6135 (6.7e−3)	0.5379 (1.8e−2)	0.5425 (2.1e−2)
WPLMF (ours)	0.6031 (3.8e−3)	0.5744 (2.9e−3)	9.9028 (1.3e−2)	0.61004 (7.5e−3)	0.7816 (8.2e−3)	0.5858 (9.4e−3)	0.5635 (4.3e−3)

The asterisk (*) indicates that SMILES data is not included in the SIDER4 dataset; instead, an 881-dimensional Pubchem fingerprint is used