iATC-FRAKEL: a simple multi-label web server for recognizing anatomical therapeutic chemical classes of drugs with their fingerprints only

Motivation: Anatomical therapeutic chemical (ATC) classification system is very important for drug utilization and studies. Correct prediction of the 14 classes in the first level for given drugs is an essential problem for the study on such system. Several multi-label classifiers have been proposed in this regard. However, only two of them provided the web servers and their performance was not very high. On the other hand, although some rest classifiers can provide better performance, they were built based on some prior knowledge on drugs, such as information of chemical-chemical interaction and chemical ontology, leading to limited applications. Furthermore, provided codes of these classifiers are almost inaccessible for pharmacologists. Results: In this study, we built a simple web server, namely iATC-FRAKEL. This web server only required the SMILES format of drugs as input and extracted their fingerprints for making prediction. The performance of the iATC-FRAKEL was much higher than all existing web servers and was comparable to the best multi-label classifier but had much wider applications.