ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions

Table 3 Average performance of various SFs on Dataset I

Scoring functions	ROC_AUC	EF_0.5%	EF_1%	EF_2%	EF_5%	F1	MCC	Kappa
Glide@sp	0.634	10.386	7.779	5.289	3.353	–	–	–
Gold@chemplp	0.725	14.025	11.078	8.380	5.262	–	–	–
Dock	0.770	–	15.769	–	–	–	–	–
sp_free_svm	0.972	41.676	41.672	40.211	22.597	0.715	0.711	0.707
sp_free_xgb	0.977	41.147	38.353	26.178	10.940	0.661	0.692	0.655
sp_free_rf	0.955	41.743	41.618	38.099	17.828	0.607	0.604	0.598
sp_all_svm	0.972	41.607	41.583	40.486	21.924	0.731	0.728	0.724
chemplp_free_svm	0.993	53.625	46.801	41.027	21.272	0.897	0.897	0.894

The average performance of the customized SFs built by 3 ML algorithms (SVM, XGBoost and RF) in terms of 7 metrics (ROC AUC, EF at 0.5% level, EF at 1% level, EF at 2% level, EF at 5% level, F1 Score, MCC and Cohen’s kappa) and the performance of 2 traditional SFs (Glide SP and ChemPLP) in terms of 4 metrics (ROC AUC, EF at 0.5% level, EF at 1% level, EF at 2% level and EF at 5% level) on the Dataset I. For the SF labels in this figure, ‘sp’ and ‘chemplp’ represent the docking methods (Glide SP and Gold ChemPLP) used for binding pose generation, ‘free’ and ‘all’ represent the descriptor combinations, and ‘svm’, ‘xgb’ and ‘rf’ are the ML algorithms used for modelling

ISSN: 1758-2946