Table 3.

Validation analysis for the training and test sets when treating the data as binary using 1000μM as the K_i upper limit for an inhibitor.^a

	true positivesb	false negativesc	false positivesd	true negativese
quantitative model training set (n=22)	14 (63.6%)	0 (0%)	1 (4.55%)	7 (31.8%)
quantitative model test set (n=27, FAST conformer)	8 (29.6%)	8 (29.6%)	0 (0%)	11(40.7%)
quantitative model test set (n=27, BEST conformer)	9 (33.3%)	6 (22.2%)	1 (3.70%)	11(40.7%)
quantitative model literature test set (n=32, FAST conformer)	2 (6.25%)	12 (37.5%)	0 (0%)	18 (56.25%)
quantitative model literature test set (n=32, BEST conformer)	2 (6.25%)	12 (37.5%)	2 (6.25%)	16 (50%)
Bayesian model training set (n=22)f	13 (59.1%)	1 (4.55%)	1 (4.55%)	7 (31.8%)
Bayesian model test set (n=27)	13 (48.1%)	3 (11.1%)	2 (7.41%)	9 (33.3%)
Bayesian model literature test set (n=32)	9 (28.1%)	5 (15.6%)	6 (18.8%)	12 (37.5%)

^aValues in table are numbers of compounds that were true positives, false negatives, false positives, or true negatives. Values in parenthesis are simply the percent of compounds that were true positives, false posi tives, true negatives, or false negatives.

^bTrue positives were both predicted and observed to be inhibitors (K_i < 1000μM).

^cFalse negatives were predicted to non- inhibitors but were inhibitors.

^dFalse positives were predicted to be inhibitors but were non-inhibitors.

^eTrue negatives were both predicted and observed to be non-inhibitors (K_i > 1000μM).

^fThe calculation was based on the leave-one-out cross-validation approach.