Table 6 Comparison with previously published models.

No.	Total number of components	Modeling techniques	Training set				Prediction set				Ref.
No.	Total number of components	Modeling techniques	R2	Q²_LOO	RMSE	S	R2	Q²	RMS	S	Ref.
1	186	MLR	0.865	0.844	0.159		0.829	0.809	0.182		⁶²
		PLS	0.865	0.846	0.159		0.829	0.809	0.182
		ANN	0.900		0.137		0.844	0.827	0.175
2	156	MLR	0.771	0.593	0.212	0.212	0.715	0.716	0.251	0.251	³
		PLS	0.766	0.674	0.214	0.214	0.718	0.718	0.250	0.250
		BPNN	0.816		0.192	0.192	0.740	0.738	0.247	0.247
3	204	ANN	0.795		0.203	0.203	0.756	0.703	0.257	0.257	⁵⁸
4	156	MLR	0.7684			0.194	0.7222			0.177	¹¹
4	156	ANN	0.8481			0.168	0.8658			0.130	¹¹
5	227	MLR	0.8141				0.6912			0.2854	⁶³
6	161	MLR	0.750	0.793	0.19		0.759	0.749	0.23		¹
7	60	MLR	0.89	0.83			0.84	0.81	0.19		¹²
			0.85	0.81			0.84	0.84	0.19
			0.88	0.83			0.85	0.85	0.14
			0.88	0.84			0.83	0.83	0.24
8	240	ANN	0.96		0.69		0.95		0.92		⁶⁴
8	240	SISSO	0.91		0.81		0.85		1.07		⁶⁴
9	156	Physical based quantitative model	0.71		0.238		0.721		0.265		⁶
10	308	Physical based quantitative model	0.8085				0.8804			0.193	⁶⁶
11	404	SMILES and HSG based	0.8039	0.7969	0.182		0.7821	0.7715	0.1935		Present work

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