pharmine

The following statistics are usually calculated to determine the predictive capability of a qSAR model.

where MCC is the Matthews correlation coefficient (Matthews 1975), TP is number of the true positives, TN is the number of true negatives, FP is number of the false positives and FN is the number of false negatives. Sensitivity (SE) and specificity (SP) are the classification accuracies of a qSAR model for the positive and negative data classes respectively. Overall accuracy (Q) is the classification accuracy of the qSAR model for both positive and negative data classes. The shortcoming of the overall accuracy is that an imbalance in the data classes may result in a high overall accuracy even if either sensitivity or specificity is low. For example, a qSAR model which has a sensitivity of 100% and specificity of 0% will have an overall accuracy of 90% for a validation set that have 9 times more compounds of the positive data class than compounds of the negative data class. Thus MCC, which is a weighted measure, is increasingly being used to measure the predictive capability of qSAR models. A MCC value of 1 indicates that the qSAR model can predict the data classes of unknown compounds perfectly, a MCC value of 0 is expected for a qSAR model that is not better than random guessing, and a MCC value of -1 indicates total disagreement between the predicted data classes and the actual data classes. For the above example, MCC will give a value of 0, which is a more accurate representation of the predictive capability of the model.

References

• Matthews BW (1975). Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta 405(2): 442-451.

This entry was posted by Yap Chun Wei on Friday, March 7th, 2008 at 10:10 pm and is filed under Data mining, Pharmacy, Tutorial. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.