A large descriptor set and a probabilistic kernel-based classifier significantly improve druglikeness classification.

Probabilistic support vector machine (SVM) in combination with ECFP_4 (Extended Connectivity Fingerprints) were applied to establish a druglikeness filter for molecules. Here, the World Drug Index (WDI) and the Available Chemical Directory (ACD) were used as surrogates for druglike and nondruglike molecules, respectively. Compared with published methods using the same data sets, the classifier significantly improved the prediction accuracy, especially when using a larger data set of 341 601 compounds, which further pushed the correct classification rates up to 92.73%. On the other hand, most characteristic features for drugs and nondrugs found by the current method were visualized, which might be useful as guiding fragments for de novo drug design and fragment based drug design.