ArticleBioMed research international2013
Predicting drugs side effects based on chemical-chemical interactions and protein-chemical interactions.
Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects.Computational and mathematical methods in medicine · 2022Article
- Literature based discovery of alternative TCM medicine for adverse reactions to depression drugs.BMC bioinformatics · 2020Article
- Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy.Computational and mathematical methods in medicine · 2020Article
- Improving inverse docking target identification with Z-score selection.Chemical biology & drug design · 2019Article
- A hierarchical anatomical classification schema for prediction of phenotypic side effects.PloS one · 2018Article
- A computational method for the identification of candidate drugs for non-small cell lung cancer.PloS one · 2017Article
- Prediction of drugs target groups based on ChEBI ontology.BioMed research international · 2013Article
- Identification of age-related macular degeneration related genes by applying shortest path algorithm in protein-protein interaction network.BioMed research international · 2013Article
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Authors and funding
7 authors.
Funding
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Abstract
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.
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