Table 5.

Ranked MACCS Fingerprint-Based Prediction Scores for Each Machine Learning Algorithm by Metrics (Average over Three Datasets)^a

algorithms	AUC	F1_score	ACC	Cohen’s κ	MCC	precision	recall	mean	rank
SVM	0.915	0.526	0.847	0.441	0.498	0.438	0.878	0.649	7
MLP_1	0.932	0.553	0.884	0.492	0.545	0.480	0.860	0.678	1
MLP_2	0.923	0.524	0.854	0.452	0.520	0.431	0.920	0.661	4
MLP_3	0.927	0.561	0.900	0.489	0.521	0.475	0.775	0.664	3
MLP_4	0.924	0.555	0.892	0.482	0.524	0.465	0.817	0.666	2
MLP_5	0.919	0.531	0.869	0.441	0.496	0.430	0.850	0.648	8
RF	0.869	0.485	0.831	0.374	0.431	0.392	0.818	0.600	10
ABDT	0.915	0.548	0.907	0.491	0.525	0.465	0.766	0.660	6
DT	0.823	0.520	0.889	0.435	0.455	0.454	0.675	0.607	9
NB	0.839	0.459	0.792	0.326	0.388	0.368	0.817	0.570	11
logistic	0.923	0.525	0.852	0.445	0.518	0.423	0.938	0.661	4

^aEach bold entry shows the highest metric value among the machine learning models using different algorithms.