Table 2.

The percentage of each substrate set with an experimentally observed SOM predicted in the top two rank-positions by the given method for Calibration (Cal.)^a and External (Ext.)^b sets^c

Isozyme Substrate Set	2C9 Cal.	2C9 Ext.	2D6 Cal.	2D6 Ext.	3A4 Cal.	3A4 Ext.
Number of Substrates	98	128	134	136	321	154
RS-Predictor (TOP SCR)	84.7	80.5	85.8	79.4	81.9	79.2
RS-Predictor (TOP QC SCR)	81.6	79.7	86.6	78.7	85.7	72.7
RS-Predictor (TOP QC)	78.6	78.9	84.3	77.2	81.0	68.8
SMARTCyp	67.7	66.9	48.5	68.1	73.1	77.2
StarDrop	77.4	78.4	81.5	69.2	77.5	66.9
Schrödinger	69.6	74.0	66.2	70.1	80.2	68.2
Schrödinger (3A4 Model)	73.5	71.9	58.5	68.1	80.2	68.2
Merckd (Sheridan et al.)	72.4	—	71.9	—	77.4	—
MetaSited (2.7.5)	68.8	—	65.4	—	61.8	—
Random Model	22.5	22.0	20.2	22.0	19.4	24.5
Avg. # Observed SOMs	1.9	1.6	1.6	1.6	1.8	2.0
Avg. # Potential SOMs	17.0	16.5	17.3	16.5	21.3	17.5

^aCross-validated RS-Predictor results for the Calibration sets were obtained from predictions made using the Training schema described in Figure 1.

^bBlind RS-Predictor results for the External sets were obtained from predictions made using the Prediction schema described in Figure 1.

^cFor each CYP, the optimal model is shown in bold, as are all other models found not to be statistically different using Fisher’s exact test of independence.

^d

Merck and MetaSite results are from original calibration and external sets released by Sheridan et al.; performance rates were merged as follows:

$$\begin{array}{l}\text{Merck}=2\text{C}9:73\ast \frac{92}{102}+67\ast \frac{10}{102}=72.4\%,2\text{D}6:72\ast \frac{124}{134}+70\ast \frac{10}{134}=71.9\%,3\text{A}4:77\ast \frac{316}{335}+84\ast \frac{19}{335}=77.4\%\\ \text{MetaSite}=2\text{C}9:69\ast \frac{92}{102}+67\ast \frac{10}{102}=68.8\%,2\text{D}6:65\ast \frac{124}{134}+70\ast \frac{10}{134}=65.4\%,3\text{A}4:65\ast \frac{124}{134}+70\ast \frac{10}{134}=65.4\%\end{array}$$

Since Merck and MetaSite models are not made public, results for these methods could not be obtained for the External sets.