Schizophrenia is a debilitating mental disorder that affects one percent of the general population and leads to an increased risk of homelessness and suicide. Because the genetic component underlying schizophrenia is not well understood, a patient's symptoms are usually the focus of treatment. With limited drug classes for treatment, schizophrenia is a prime candidate for drug repurposing. Over the last decade, drug development has remained stagnant even with the reference genome and an abundance of experimental data. Due to the high development cost and likelihood of failing during clinical trials, drug discovery has focused on an estimated 3000 druggable human genes; however, this narrow focus has severely limited the potential for discovery. With an estimated 3000 druggable human genes, great potential remains for discovery of new drugs and novel uses for current drugs from a variety of sources, including literature and gene expression data. Literature is the most comprehensive resource for known information and, more specifically, high quality low- throughput data. Due to the availability of high-throughput experimental data, many computational methods for drug discovery and repurposing have focused on incorporating this data into their algorithms; however, data extracted from the literature is equally valuable. To investigate druggability using literature and experimental data, I propose the following specific aims: (1) integrate drug, gene, and disease relationships from the literature and experimental data sources; (2) infer druggable proteins using networks of protein, drug, and disease interactions with experimental data; and (3) apply the network model to schizophrenia to predict novel druggable proteins for drug repurposing. With success, I will create a novel methodology to integrate literature and high-throughput experimental data and apply this methodology to discover novel druggable proteins related to schizophrenia that can inform future drug repurposing studies.