@inproceedings{5335863459d84ae89c680414d9909de1,
title = "Prediction of Drug Classes with a Deep Neural Network using Drug Targets and Chemical Structure Data",
abstract = "Drugs are classified according to their biological and chemical reactions, and the systems that they target. Thus, an accurate and efficient prediction method for drug class discovery would reveal key properties of candidate drugs, significantly conserving time and resources in drug repositioning and design. Previous approaches, based on data mining or statistics, required complicated feature construction in advance. Knowing that deep learning can identifying patterns in high-dimensional datasets without elaborate feature selection or engineering, we constructed a model for predicting drug classes using deep neural networks - with biological and chemical structure data. Our proposed model outperforms previous learning-based methods in terms of prediction accuracy.",
keywords = "convolutional neural network (CNN), deep learning, drug, prediction, recurrent neural network (RNN)",
author = "Jeonghee Jo and Choi, \{Hyun Soo\} and Sungroh Yoon",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",
year = "2019",
month = nov,
doi = "10.1109/BIBM47256.2019.8983104",
language = "English",
series = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "664--667",
editor = "Illhoi Yoo and Jinbo Bi and Hu, \{Xiaohua Tony\}",
booktitle = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",
}