  Motivation: Automatic Anatomical Therapeutic Chemical (ATC) classification is
a critical and highly competitive area of research in bioinformatics because of
its potential for expediting drug develop-ment and research. Predicting an
unknown compound's therapeutic and chemical characteristics ac-cording to how
these characteristics affect multiple organs/systems makes automatic ATC
classifica-tion a challenging multi-label problem. Results: In this work, we
propose combining multiple multi-label classifiers trained on distinct sets of
features, including sets extracted from a Bidirectional Long Short-Term Memory
Network (BiLSTM). Experiments demonstrate the power of this approach, which is
shown to outperform the best methods reported in the literature, including the
state-of-the-art developed by the fast.ai research group. Availability: All
source code developed for this study is available at
https://github.com/LorisNanni. Contact: loris.nanni@unipd.it
