Title: DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction,
Abstract: Chemical-chemical interaction (CCI) plays a key role in predicting candidate
drugs, toxicity, therapeutic effects, and biological functions. In various
types of chemical analyses, computational approaches are often required due to
the amount of data that needs to be handled. The recent remarkable growth and
outstanding performance of deep learning have attracted considerable research
attention. However,even in state-of-the-art drug analysis methods, deep
learning continues to be used only as a classifier, although deep learning is
capable of not only simple classification but also automated feature
extraction. In this paper, we propose the first end-to-end learning method for
CCI, named DeepCCI. Hidden features are derived from a simplified molecular
input line entry system (SMILES), which is a string notation representing the
chemical structure, instead of learning from crafted features. To discover
hidden representations for the SMILES strings, we use convolutional neural
networks (CNNs). To guarantee the commutative property for homogeneous
interaction, we apply model sharing and hidden representation merging
techniques. The performance of DeepCCI was compared with a plain deep
classifier and conventional machine learning methods. The proposed DeepCCI
showed the best performance in all seven evaluation metrics used. In addition,
the commutative property was experimentally validated. The automatically
extracted features through end-to-end SMILES learning alleviates the
significant efforts required for manual feature engineering. It is expected to
improve prediction performance, in drug analyses.