SuperPred 3.0: drug classification and target prediction—a machine learning approach

Since the last published update in 2014, the SuperPred webserver has been continuously developed to offer state-of-the-art models for drug classification according to ATC classes and target prediction. For the first time, a thoroughly filtered ATC dataset, that is suitable for accurate predictions, is provided along with detailed information on the achieved predictions. This aims to overcome the challenges in comparing different published prediction methods, since performance can vary greatly depending on the training dataset used. Additionally, both ATC and target prediction have been reworked and are now based on machine learning models instead of overall structural similarity, stressing the importance of…

• DF
  Deutsche Forschungsgemeinschaft

  Awards: KFO339, TRR295
• C–
  Charité – Universitätsmedizin Berlin