Convolutional Neural Networks for ATC Classification.
Journal:
Current pharmaceutical design
Published Date:
Jan 1, 2018
Abstract
BACKGROUND: Anatomical Therapeutic Chemical (ATC) classification of unknown compound has raised high significance for both drug development and basic research. The ATC system is a multi-label classification system proposed by the World Health Organization (WHO), which categorizes drugs into classes according to their therapeutic effects and characteristics. This system comprises five levels and includes several classes in each level; the first level includes 14 main overlapping classes. The ATC classification system simultaneously considers anatomical distribution, therapeutic effects, and chemical characteristics, the prediction for an unknown compound of its ATC classes is an essential problem, since such a prediction could be used to deduce not only a compound's possible active ingredients but also its therapeutic, pharmacological, and chemical properties. Nevertheless, the problem of automatic prediction is very challenging due to the high variability of the samples and the presence of overlapping among classes, resulting in multiple predictions and making machine learning extremely difficult.