Over 90% of drug candidates fail in clinical trials, while it takes 10-15 years and 1 billion US dollars to develop one successful drug. Drug innovation is especially poor for psychiatric disorders, where a lack of objective biomarkers for psychiatric diagnoses and the assessment of treatment outcomes contributes to clinical trial failure. In addition, disease comorbidity and complex symptom profiles among psychiatric disorders obscure the identification of causal mechanisms for therapeutic intervention. Therefore, new approaches are urgently needed to identify more suitable drug candidates for clinical trials. One approach is integrating human genetic data into the drug candidate selection process. Genome-wide association studies have identified thousands of replicable risk loci for psychiatric disorders, and sophisticated statistical tools are increasingly effective at using these data to pinpoint likely causal genes. These studies have also uncovered shared or pleiotropic genetic risk factors underlying comorbid psychiatric disorders. The study of shared genetic effects between psychiatric disorders may provide new opportunities for novel therapeutics that target a common mechanism rather than treating each disease separately. In this article, we argue that leveraging pleiotropic effects will provide opportunities for the discovery of novel drug targets and facilitate the identification of more effective treatments for psychiatric disorders.