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End-to-End Representation Learning for Chemical-Chemical Interaction Prediction by Sunyoung Kwon; Sungroh Yoon is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is End-to-End Representation Learning for Chemical-Chemical Interaction Prediction about?

Chemical-chemical interaction (CCI) plays a major role in predicting candidate drugs, toxicities, therapeutic effects, and biological functions. CCI is typically inferred from a variety of information; however, CCI has yet not been predicted using a learning-based approach. In other drug analyses, deep learning has been actively used in recent years. However, in most cases, deep learning has been used only for classification even though it has feature extraction capabilities. Thus, in this paper, we propose an end-to-end representation learning method for CCI, named DeepCCI, which includes feature extraction and a learning-based approach. Our proposed architecture is based on the Siamese network. Hidden representations are extracted from a simplified molecular input line entry system (SMILES), which is a string notation representing the chemical structure using weight-shared convolutional neural networks. Subsequently, L1 element-wise distances between the two extracted hidden representations are measured. The performance of DeepCCI is compared with those of 12 fingerprint-method combinations. The proposed DeepCCI shows the best performance in most of the evaluation metrics used. I

Who reads End-to-End Representation Learning for Chemical-Chemical Interaction Prediction?

Author

Sunyoung Kwon; Sungroh Yoon

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2018

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)