ABSTRACT
A natural application of predictive modeling in psychiatry is computational drug repositioning. This chapter aims to survey the current state of repositioning opportunities in psychiatry. It illustrates the types of large-scale biological data sets that are of value for repositioning efforts in psychiatry. The chapter emphasizes the translational potential of this method by profiling select efforts that have already been made to integrate psychiatric data types. It anticipates where such efforts might fall short and propose expanding the notion of repositioning to include components of the therapeutic discovery process beyond the active molecule. The contribution of rare variation to psychiatric disease has been studied through analyses of copy number and single-nucleotide variants. As comprehensive data sets of the structural and functional connectivity of the brain in different psychiatric disease states become available, integrating this information into predictive models will enable the systematic identification of repositioning opportunities that extend beyond the active molecule.
