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Motivation: Anatomical Therapeutic Chemical (ATC) classification system, widely applied in almost all drug utilization studies, is currently the most widely recognized classification system for drugs. Currently, new drug entries are added into the system only on users’ requests, which leads to seriously incomplete drug coverage of the system, and bioinformatics prediction is helpful during this process.

Results: Here we propose a novel prediction model of drug-ATC code associations, using logistic regression to integrate multiple heterogeneous data sources including chemical structures, target proteins, gene expression, side-effects and chemical–chemical associations. The model obtains good performance for the prediction not only on ATC codes of unclassified drugs but also on new ATC codes of classified drugs assessed by cross-validation and independent test sets, and …