CNN-DDI: A novel deep learning method for predicting drug-drug interactions

Chengcheng Zhang; Tianyi Zang

doi:10.1109/BIBM49941.2020.9313404

CNN-DDI: A novel deep learning method for predicting drug-drug interactions

Chengcheng Zhang
, Tianyi Zang^*

^*Corresponding author for this work

Faculty of Computing

Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

Predicting drug-drug interactions (DDIs) is one of the major concerns in patients' medication, which is crucial for patient safety and public health. Most of studies study whether drugs interact or not. In this study, we focus on 65 categories of drug-drug interaction-associated events and proposed a new method based on convolutional neural network (CNN), named CNN-DDI, for predicting DDIs. First, the categories, targets, pathways and enzymes of drugs were extracted as the features of drugs, which constructed the input of CNN-DDI. Then, these features were as input vectors of our CNN network, and the output is the prediction of drug-drug interaction-associated events' categories. In the computational experiments, the CNN-DDI method achieves an accuracy rate up to 0.8914, an area under the precision-recall curve up to 0.9322. And the experiments also prove using feature combinations outperforms one feature. Compared with other state-of-the-art methods, the CNN-DDI method has better performance in the superiority and the effectiveness for predicting DDI's events.

Original language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Editors	Taesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1708-1713
Number of pages	6
ISBN (Electronic)	9781728162157
DOIs	https://doi.org/10.1109/BIBM49941.2020.9313404
State	Published - 16 Dec 2020
Event	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of Duration: 16 Dec 2020 → 19 Dec 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Country/Territory	Korea, Republic of
City	Virtual, Seoul
Period	16/12/20 → 19/12/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

CNN
Drug-drug interactions
drug categories
feature combination

Access to Document

10.1109/BIBM49941.2020.9313404

Cite this

Zhang, C., & Zang, T. (2020). CNN-DDI: A novel deep learning method for predicting drug-drug interactions. In T. Park, Y.-R. Cho, X. T. Hu, I. Yoo, H. G. Woo, J. Wang, J. Facelli, S. Nam, & M. Kang (Eds.), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 (pp. 1708-1713). Article 9313404 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM49941.2020.9313404

Zhang, Chengcheng ; Zang, Tianyi. / CNN-DDI : A novel deep learning method for predicting drug-drug interactions. Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. editor / Taesung Park ; Young-Rae Cho ; Xiaohua Tony Hu ; Illhoi Yoo ; Hyun Goo Woo ; Jianxin Wang ; Julio Facelli ; Seungyoon Nam ; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1708-1713 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

@inproceedings{d1f85f28042d4d0f969626503ddd4bb1,

title = "CNN-DDI: A novel deep learning method for predicting drug-drug interactions",

abstract = "Predicting drug-drug interactions (DDIs) is one of the major concerns in patients' medication, which is crucial for patient safety and public health. Most of studies study whether drugs interact or not. In this study, we focus on 65 categories of drug-drug interaction-associated events and proposed a new method based on convolutional neural network (CNN), named CNN-DDI, for predicting DDIs. First, the categories, targets, pathways and enzymes of drugs were extracted as the features of drugs, which constructed the input of CNN-DDI. Then, these features were as input vectors of our CNN network, and the output is the prediction of drug-drug interaction-associated events' categories. In the computational experiments, the CNN-DDI method achieves an accuracy rate up to 0.8914, an area under the precision-recall curve up to 0.9322. And the experiments also prove using feature combinations outperforms one feature. Compared with other state-of-the-art methods, the CNN-DDI method has better performance in the superiority and the effectiveness for predicting DDI's events.",

keywords = "CNN, Drug-drug interactions, drug categories, feature combination",

author = "Chengcheng Zhang and Tianyi Zang",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 ; Conference date: 16-12-2020 Through 19-12-2020",

year = "2020",

month = dec,

day = "16",

doi = "10.1109/BIBM49941.2020.9313404",

language = "英语",

series = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1708--1713",

editor = "Taesung Park and Young-Rae Cho and Hu, \{Xiaohua Tony\} and Illhoi Yoo and Woo, \{Hyun Goo\} and Jianxin Wang and Julio Facelli and Seungyoon Nam and Mingon Kang",

booktitle = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",

address = "美国",

}

Zhang, C & Zang, T 2020, CNN-DDI: A novel deep learning method for predicting drug-drug interactions. in T Park, Y-R Cho, XT Hu, I Yoo, HG Woo, J Wang, J Facelli, S Nam & M Kang (eds), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020., 9313404, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1708-1713, 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Virtual, Seoul, Korea, Republic of, 16/12/20. https://doi.org/10.1109/BIBM49941.2020.9313404

CNN-DDI: A novel deep learning method for predicting drug-drug interactions. / Zhang, Chengcheng; Zang, Tianyi.
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. ed. / Taesung Park; Young-Rae Cho; Xiaohua Tony Hu; Illhoi Yoo; Hyun Goo Woo; Jianxin Wang; Julio Facelli; Seungyoon Nam; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1708-1713 9313404 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - CNN-DDI

T2 - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

AU - Zhang, Chengcheng

AU - Zang, Tianyi

PY - 2020/12/16

Y1 - 2020/12/16

N2 - Predicting drug-drug interactions (DDIs) is one of the major concerns in patients' medication, which is crucial for patient safety and public health. Most of studies study whether drugs interact or not. In this study, we focus on 65 categories of drug-drug interaction-associated events and proposed a new method based on convolutional neural network (CNN), named CNN-DDI, for predicting DDIs. First, the categories, targets, pathways and enzymes of drugs were extracted as the features of drugs, which constructed the input of CNN-DDI. Then, these features were as input vectors of our CNN network, and the output is the prediction of drug-drug interaction-associated events' categories. In the computational experiments, the CNN-DDI method achieves an accuracy rate up to 0.8914, an area under the precision-recall curve up to 0.9322. And the experiments also prove using feature combinations outperforms one feature. Compared with other state-of-the-art methods, the CNN-DDI method has better performance in the superiority and the effectiveness for predicting DDI's events.

AB - Predicting drug-drug interactions (DDIs) is one of the major concerns in patients' medication, which is crucial for patient safety and public health. Most of studies study whether drugs interact or not. In this study, we focus on 65 categories of drug-drug interaction-associated events and proposed a new method based on convolutional neural network (CNN), named CNN-DDI, for predicting DDIs. First, the categories, targets, pathways and enzymes of drugs were extracted as the features of drugs, which constructed the input of CNN-DDI. Then, these features were as input vectors of our CNN network, and the output is the prediction of drug-drug interaction-associated events' categories. In the computational experiments, the CNN-DDI method achieves an accuracy rate up to 0.8914, an area under the precision-recall curve up to 0.9322. And the experiments also prove using feature combinations outperforms one feature. Compared with other state-of-the-art methods, the CNN-DDI method has better performance in the superiority and the effectiveness for predicting DDI's events.

KW - CNN

KW - Drug-drug interactions

KW - drug categories

KW - feature combination

UR - https://www.scopus.com/pages/publications/85100351116

U2 - 10.1109/BIBM49941.2020.9313404

DO - 10.1109/BIBM49941.2020.9313404

M3 - 会议稿件

AN - SCOPUS:85100351116

T3 - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

SP - 1708

EP - 1713

BT - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

A2 - Park, Taesung

A2 - Cho, Young-Rae

A2 - Hu, Xiaohua Tony

A2 - Yoo, Illhoi

A2 - Woo, Hyun Goo

A2 - Wang, Jianxin

A2 - Facelli, Julio

A2 - Nam, Seungyoon

A2 - Kang, Mingon

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 16 December 2020 through 19 December 2020

ER -

Zhang C, Zang T. CNN-DDI: A novel deep learning method for predicting drug-drug interactions. In Park T, Cho YR, Hu XT, Yoo I, Woo HG, Wang J, Facelli J, Nam S, Kang M, editors, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1708-1713. 9313404. (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). doi: 10.1109/BIBM49941.2020.9313404

CNN-DDI: A novel deep learning method for predicting drug-drug interactions

Abstract

Publication series

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UN SDGs

Keywords

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