Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: Chemical-chemical interaction (CCI) plays a key role in predicting candidate drugs, toxicity, therapeutic eects, and biological functions. CCI was created from text mining, experiments, similarities, and databases; to date, no learning-based CCI prediction method exist. In chemical analyses, computational approaches are required. e recent remarkable growth and outstanding performance of deep learning have aracted considerable research aention. However, even in state-of-the-art drug analyses, deep learning continues to be used only as a classier. Nevertheless, its purpose includes not only simple classication, but also automated feature extraction. In this paper, we propose the rst end-to-end learning method for CCI, named DeepCCI. Hidden features are derived from a simplied molecular input line entry system (SMILES), which is a string notation representing the chemical structure, instead of learning from craed 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. e performance of DeepCCI was compared with a plain deep classier and conventional machine learning methods. e proposed DeepCCI showed the best performance in all seven evaluation metrics used. In addition, the commutative property was experimentally validated. e automatically extracted features through end-to-end SMILES learning alleviates the signicant efforts required for manual feature engineering. It is expected to improve prediction performance, in drug analyses.

DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction