In independent tests, the StarDrop predictive ADME models have matched or exceeded the performance of other commercially available or published models. The results for the StarDrop models are shown in the tables below. If comparing the StarDrop models with other in silico models, it is important to note that the reported validation results here are for independent test sets (see “How do you validate your models?” above). It is also important, when comparing StarDrop predictions with experimental data, to ensure that this is a like-for-like comparison. For example, HIA predictions cannot be compared directly with oral bioavailability data on which both absorption and first-pass clearance will have an impact. Similarly, the StarDrop predictions for aqueous solubility are unlikely to correlate well with data obtained in a high-throughput kinetic assay based on dilution of DMSO compound stocks and will not predict the solubilities of different salt forms.
Summary of Statistical Results for Continuous QSAR models
||Predicts the logarithm of the octanol/water partition coefficient for neutral compounds
||Predicts the logarithm of the octanol/buffer at pH 7.4 distribution coefficient
||Predicts the logarithm of the intrinsic aqueous solubility, S in uM, for neutral compounds
||Predicts the logarithm of the solubility, S in uM, in phosphate buffered saline at pH7.4
||Predicts the logarithm of the Brain/Blood ratio
||Predicts the pIC50 values for inhibition of hERG K+ channels expressed in mammalian cells
||Predicts the pKi values for CYP2C9 affinity
a) N = number of compounds in independent test set.
b) R2 gives the correlation between calculated and experimental values for the compounds in the independent test set.
c) The root mean squared error (RMSE) statistic gives the error for the corresponding correlation coefficient. When possible, the RMSE values are calculated for compounds within (IN) or outside (OUT) the chemical space of the model. “unknown” is reported if there are not enough training and test compounds outside the chemical space to calculate an RMSE value.
Summary of Statistical Results for Classification QSAR models
||Returns a binary prediction for human intestinal absorption, based on a threshold of 30% absorbed
||Returns a binary prediction for Blood/Brain barrier penetration
||Returns a binary prediction for P-gp transport
||Returns a binary prediction for human plasma protein binding, based on a threshold of 90% absorbed
|2D6 affinity category
||Returns a 4-class prediction for 2D6 affinity
||Root mean square error = 0.87 classes
>a) N = number of compounds in independent test set
b) The accuracy for each class is reported as the percentage of compounds correctly classified.
c) The specificity refers to the percentage of correct classifications within the overall set of compounds predicted to be in that class.