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    Tool Name:  

DDI Predictor   Downloaded: 0   Viewed: 0
Developed By: Department of Molecular Systems Biology

    Introduction:  

Identifying drug-drug interactions (DDIs) is a major challenge in drug development. Previous attempts have established formal approaches for pharmacokinetic (PK) DDIs, but there is not a feasible solution for pharmacodynamic (PD) DDIs because the endpoint is often a serious adverse event rather than a measurable change in drug concentration. Here, we developed a metric “S-score” that measures the strength of network connection between drug targets to predict PD DDIs. Utilizing known PD DDIs as golden standard positives (GSPs), we observed a significant correlation between S-score and the likelihood a PD DDI occurs. Our prediction was robust and surpassed existing methods as validated by two independent GSPs. Analysis of clinical side effect data suggested that the drugs having predicted DDIs have similar side effects. We further incorporated this clinical side effects evidence with S-score to increase the prediction specificity and sensitivity through a Bayesian probabilistic model. We have predicted 9,626 potential PD DDIs at the accuracy of 82% and the recall of 62%. Importantly, our algorithm provided opportunities for better understanding the potential molecular mechanisms or physiological effects underlying DDIs, as illustrated by case studies.

    Downloads :  

DDI Predictor is developed and maintained by CAS-MPG Partner Institute for Computational Biology(PICB) ,Chinese Academy of Sciences. The software is only free of charge for non-commercial users.

    Reference:  

PLoS Comp Biol, 2013(http://www.picb.ac.cn/hanlab/paper/Huang.journal.pcbi.2013.pdf)

    Contact Information:  

If you have any feedback or question concerning the tools you can further click the Project Website , or please feel free to contact us at jdhanpicb.ac.cn.