This research exploits the assumption that drugs and small molecules may interact with many proteins and other macromolecules, and that these interactions influence their therapeutic or toxic effects in humans. We use large-scale drug-protein interaction profiles and Bayesian statistics to create computational models, in order to repurpose drugs for novel indications and to predict the potential for specific drug-drug induced adverse health effects when using combinations of pharmaceutical drugs.
Liu, R., M. D. M. AbdulHameed, K. Kumar, X. Yu, A. Wallqvist, and J. Reifman. Data-driven prediction of adverse drug reactions induced by drug-drug interactions. BMC Pharmacology and Toxicology. 2017 June 8; 18:44. [PDF]
Liu, R., N. Singh, G. J. Tawa, A. Wallqvist, and J. Reifman. Exploiting large-scale drug-protein interaction information for computational drug repurposing. BMC Bioinformatics. 2014 June 20; 15:210. [PDF]
AbdulHameed, M. D., S. Chaudhury, N. Singh, H. Sun, A. Wallqvist, and G. J. Tawa. Exploring polypharmacology using a ROCS-based target fishing approach. Journal of Chemical Information and Modeling. 2012 February 27; 52(2):492-505. [PDF]