Table 3.

Comparison of the performance of the different methods in the target-specific case

Rewarding scheme	Dataset	Validity	Desirability	Uniqueness	Diversity	Purine ring	Furan ring	Benzene ring
	LIGAND	100.00%	14.63%	100.00%	0.67	28.27%	50.61%	71.84%
PF	DrugEx v1	98.07%	48.42%	87.32%	0.73	29.65%	61.61%	70.99%
	DrugEx v2	99.53%	89.49%	90.55%	0.73	23.73%	56.23%	67.40%
	ORGANIC	98.29%	86.98%	80.30%	0.64	10.60%	89.27%	65.28%
	REINVENT	99.59%	70.66%	99.33%	0.79	3.85%	33.82%	92.53%
WS	DrugEx v1	97.61%	44.96%	95.89%	0.68	78.92%	80.21%	68.02%
	DrugEx v2	99.62%	97.86%	90.54%	0.31	19.58%	98.56%	51.87%
	ORGANIC	98.97%	88.14%	84.13%	0.49	9.68%%	96.66%	71.48%
	REINVENT	99.55%	81.27%	98.87%	0.34	25.13%	97.52%	74.61%

Shown are validity, desirability, uniqueness, and substructure distributions of SMILES generated by four different methods in the target-specific case with PF and WS rewarding schemes. For the validity, desirability and uniqueness, the highest values are bold, while for the distribution of substructures, the bold data are labeled as the most closed to the values in the LIGAND set