. 2022 Feb 3;27(3):1039. doi: 10.3390/molecules27031039

Table 3.

The performance of different models on the balanced test set.

Partition Criterion	MODEL	AUC	ACC	MCC
SRP:2	DNN-PCD¹	0.576	0.511	0.044
SRP:2	RF-PCD²	0.632	0.592	0.183
SRP:2	CMPNN	0.784	0.711	0.423
SRP:2	DNN-ECFP³	0.72	0.667	0.333
SRP:2	SYBA	0.491	0.505	0.02
SRP:2	SYBA-2⁴	0.739	0.668	0.343
SRP:2	SAScore	0.535	0.501	−0.003
SRP: 2	SCScore	0.613	0.55	0.128
SRP:3	DNN-PCD¹	0.615	0.584	0.168
SRP:3	RF-PCD²	0.627	0.588	0.177
SRP:3	CMPNN	0.791	0.715	0.434
SRP:3	DNN-ECFP³	0.751	0.687	0.373
SRP:3	SYBA	0.465	0.496	−0.012
SRP:3	SYBA-2⁴	0.76	0.69	0.382
SRP:3	SAScore	0.513	0.5	−0.011
SRP: 3	SCScore	0.621	0.543	0.116
SRP:4	DNN-PCD¹	0.6	0.565	0.132
SRP:4	RF-PCD²	0.627	0.583	0.168
SRP:4	CMPNN	0.814	0.733	0.466
SRP:4	DNN-ECFP³	0.802	0.732	0.465
SRP:4	SYBA	0.448	0.491	−0.061
SRP:4	SYBA-2⁴	0.8	0.727	0.453
SRP:4	SAScore	0.45	0.512	−0.021
SRP:4	SCScore	0.591	0.517	0.082

DNN-PCD¹: DNN classifier built according to physicochemical descriptors. RF-PCD²: RF classifier built on physicochemical descriptor. DNN-ECFP³: DNN classifier built on ECFP4 descriptor. SYBA-2⁴: retrained the SYBA model on our own dataset.