Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions
A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industr...
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Online Access: | http://dx.doi.org/10.1155/2013/485034 |
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doaj-1c26fe1d29744395ac0a1a708cbe9d572020-11-24T22:50:15ZengHindawi LimitedBioMed Research International2314-61332314-61412013-01-01201310.1155/2013/485034485034Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical InteractionsLei Chen0Tao Huang1Jian Zhang2Ming-Yue Zheng3Kai-Yan Feng4Yu-Dong Cai5Kuo-Chen Chou6College of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaKey Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, ChinaDepartment of Ophthalmology, Shanghai First People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200080, ChinaState Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Shanghai 201203, ChinaBeijing Genomics Institute, Shenzhen Beishan Industrial Zone, Shenzhen 518083, ChinaInstitute of Systems Biology, Shanghai University, Shanghai 200444, ChinaGordon Life Science Institute, Belmont, Massachusetts 02478, USAA drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine.http://dx.doi.org/10.1155/2013/485034 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lei Chen Tao Huang Jian Zhang Ming-Yue Zheng Kai-Yan Feng Yu-Dong Cai Kuo-Chen Chou |
spellingShingle |
Lei Chen Tao Huang Jian Zhang Ming-Yue Zheng Kai-Yan Feng Yu-Dong Cai Kuo-Chen Chou Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions BioMed Research International |
author_facet |
Lei Chen Tao Huang Jian Zhang Ming-Yue Zheng Kai-Yan Feng Yu-Dong Cai Kuo-Chen Chou |
author_sort |
Lei Chen |
title |
Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions |
title_short |
Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions |
title_full |
Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions |
title_fullStr |
Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions |
title_full_unstemmed |
Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions |
title_sort |
predicting drugs side effects based on chemical-chemical interactions and protein-chemical interactions |
publisher |
Hindawi Limited |
series |
BioMed Research International |
issn |
2314-6133 2314-6141 |
publishDate |
2013-01-01 |
description |
A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine. |
url |
http://dx.doi.org/10.1155/2013/485034 |
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