QSAR models for tyrosinase inhibitory activity description applying modern statistical classification techniques: A comparative study

Cluster analysis (CA), Linear and Quadratic Discriminant Analysis (L(Q)DA), Binary Logistic Regression (BLR) and Classification Tree (CT) are applied on two datasets for description of tyrosinase inhibitory activity from molecular structures. The first set included 701 tyrosinase inhibitors (TI) that are used for performance of inhibitory and non-inhibitory activity and the second one is for potency estimation of active compounds. 2D TOMOCOMD-CARDD atom-based quadratic indices are computed as molecular descriptors. CA is used to “rational” design of training (TS) and prediction set (PS) but it shows of not being adequate as classification technique. On the first data, the overall accuracies (Q) are 91.42%, 92.35% 91.88%, 91.79% for TS, and 91.04%, 92.43%, 88.24%, 89.36% for PS in LDA, QDA BLR and CT-based model, respectively, while the corresponding values obtained on the second one are 89.95%, 90.70%, 90.20%, 89.20% for TS and 83.71%, 84.44%, 82.96%, 82.22% for PS. A comparative analysis of used statistical techniques is held out taking into consideration generated posterior probability, accuracy, required assumptions and the form of predictor variables used. On the two datasets, results depicted by Receiver Operating Characteristic (ROC) curves together with Multiple Comparison Procedures (MCP) show that QDA has in general the best behavior as classification algorithm. The results suggest that it will be possible to produce a better description of tyrosinase activity applying the statistical techniques presented in this report, which could increase the practicality of the in silico data mining for the discovery of novel TIs.