In recent years, a research on drug discovery to find the new medical indication from the old drugs and to redevelop them as a treatment for another disease has attracted attention as drug re-positioning in order to minimize cost and a development period for efficiency [3]. One reason is complex biological processes of most human diseases which the ’one-drug-one-gene’ approach is not effective to treat [4]. In addition, the official report on the side effects of prescription drugs that have increased dramatically over the past decade. In 2011, U.S. Food and Drug Administration (FDA) has received about 500,000 reports of health hazards and the death related to medical products per year [5]. In considering the safety of the drug, the prediction of side effect in drug discovery is an important issue for drug screening. Then, the personalized medicine is expected to develop new drugs and treatments, and to avoid side effect using Single Nucleotide Polymorphism (SNP) which will contribute to identifying genes related to the diseases [6]. More than one SNP are in a gene. Here, previous works on potential side effect prediction of drug candidates and drug discovery with SNPs using Big Data are reviewed.