Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction.

ABSTRACT: De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification mo

SUBMITTER: Coelho ED 

PROVIDER: S-EPMC5125559 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature