Table 3.

The correlation coefficients between cell-based assay data and computational predictions. The key component(s) of each physical model is/are marked with “X”. Physics-based predictions with correlation coefficient differences (Δr² or Δρ) relative to LogK(ΔG_{transfer(LDC)}) ≥0.05 and ≤-0.05 are highlighted in green and red, respectively. QikProp predictions with correlation coefficient differences relative to LogP_m (Δ|r²| or Δ|ρ|) ≥0.05 are highlighted in light green.

	Model’s component						Correlation coefficient
Predictiona	Conf. search	Conf. ensemble	State penalty	Conf. penalty	Size selectivity	Membrane diffusion	Avdeef (Caco-2)		Balimane (Caco-2)		Bermejo (Caco-2)		Fujikawa (Caco-2)		Goodwin (Caco-2)		Irvine (Caco-2)		Irvine (MDCK)		Li (Caco-2)
							r2	ρ	r2	ρ	r2	ρ	r2	ρ	r2	ρ	r2	ρ	r2	ρ	r2	ρ
LogK(ΔGtransfer(LDC))	×						0.67	0.89	0.56	0.44b	0.39	0.50b	0.32	0.35b	0.83	0.89	0.59	0.77	0.68	0.83	0.49	0.61
LogK(ΔGtransfer)	×	×					0.70	0.89	0.55	0.44b	0.37	0.45b	0.33	0.35b	0.88	0.96	0.57	0.75	0.67	0.83	0.55	0.69
LogK(ΔGtransfer+cGstate)	×	×	×				0.80	0.89	0.55	0.56b	0.58	0.52b	0.35	0.43b	0.88	0.96	0.68	0.80	0.74	0.84	0.49	0.66
LogK(ΔGtransfer+cGcf)	×	×		×			0.75	0.89	0.53	0.54b	0.44	0.47b	0.30	0.30b	0.82	0.92	0.61	0.78	0.70	0.84	0.54	0.65
LogK(ΔGc/w)	×	×	×	×			0.83	0.89	0.50	0.56b	0.61	0.55b	0.32	0.41b	0.82	0.92	0.69	0.82	0.75	0.84	0.45	0.64
LogKbarrier	×	×	×	×	×		0.84	0.89	0.55	0.56b	0.59	0.55b	0.34	0.37b	0.86	0.95	0.70	0.81	0.77	0.84	0.45	0.66
LogPm	×	×	×	×	×	×	0.84	0.89	0.56	0.56b	0.59	0.55b	0.34	0.37b	0.86	0.95	0.70	0.81	0.77	0.84	0.45	0.66
MW	Physiochemical descriptor						0.00	0.14b	0.31	−0.33b	0.00	−0.01b	0.08	−0.01b	0.05	−0.23b	0.12	−0.43	0.19	−0.43	0.04	0.18b
Vol	Physiochemical descriptor						0.06	0.37b	0.13	−0.51b	0.01	−0.02b	0.00	0.02b	0.01	0.01b	0.02	−0.21b	0.05	−0.23b	0.06	0.23b
PSA	Physiochemical descriptor						0.41	−0.71b	0.59	−0.59b	0.07	−0.17b	0.34	−0.20b	0.83	−0.89	0.54	−0.79	0.63	−0.78	0.30	−0.66
QPlogPo/w	QSPR regression-based model						0.74	0.71b	0.50	0.71	0.39	0.38b	0.32	0.55	0.53	0.76	0.27	0.48	0.31	0.54	0.15	0.45
LogQPPCaco	QSPR regression-based model						0.91	0.94	0.47	0.66b	0.42	0.62	0.81	0.81	0.84	0.90	0.74	0.85	0.81	0.88	0.55	0.72
LogQPPMDCK	QSPR regression-based model						0.91	0.94	0.51	0.66b	0.32	0.47b	0.68	0.74	0.76	0.83	0.73	0.83	0.81	0.87	0.56	0.68
N(Total)c							6		9		12		20		12		31		32		25
N(QikProp-TS)d							6		7		0		12		0		31		32		0

^aOptimal values were determined from the refined conformational ensemble for each physics-based prediction, except for ΔG_{transfer(LDC)}, which were computed using the LDC, and for LogK_barrier, which was computed using the conformation with the optimal LogP_m value.

^bThe p-value of ρ is greater than 0.05.

^cNumber of compounds included in the analysis.

^dNumber of compounds in QikProp training sets (QPlogPo/w, QPPCaco, and QPPMDCK). The physics-based model does not use a training set.