A large-scale computational approach to drug repositioning.

We have developed a computational pipeline for the prediction of protein-small molecule interactions and have applied it to the drug repositioning problem through a large-scale analysis of known drug targets and small molecule drugs. Our pipeline combines forward and inverse docking, the latter of which is a twist on the conventional docking procedure used in drug discovery: instead of docking many compounds against a specific target to look for potential inhibitors, one compound is docked against many proteins to search for potential targets. We collected an extensive set of 1,055 approved small molecule drugs and 1,548 drug target binding pockets (representing 78 unique human protein therapeutic targets) and performed a large-scale docking using ICM software to both validate our method and predict novel protein-drug interactions. For the 37 known protein-drug interactions in our data set that have a known structure complex, all docked conformations were within 2.0A of the solved conformation, and 30 of these had a docking score passing the typical ICM score threshold. Out of the 237 known protein-drug interactions annotated by DrugBank, 74 passed the score threshold, and 52 showed the drug docking to another protein with a better docking score than to its known target. These protein targets are implicated in human diseases, so novel protein-drug interactions discovered represent potential novel indications for the drugs. Our results highlight the promising nature of the inverse docking method for identifying potential novel therapeutic uses for existing drugs.