John M. Prager, Jennifer J. Liang, et al.
AMIA Joint Summits on Translational Science 2017
Drug-drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug-human protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user's molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at http://cpi.bio-x.cn/ddi/. © 2014 The Author(s).
John M. Prager, Jennifer J. Liang, et al.
AMIA Joint Summits on Translational Science 2017
Zhen Xia, Gulei Jin, et al.
Bioinformatics
Toby G. Rossman, Ekaterina I. Goncharova, et al.
Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis
Laxmi Parida
Journal of Computational Biology