In drug discovery, the role of multiple receptors based therapeutics is found to be a highly critical approach in producing high efficacy with reduced side effects drug molecules. Hepatitis-C is found to be a threatful chronic disease, even ending up in death in many cases. In the present study, we have selected a few drugs which are found highly potent against Hepatitis-c and have studied their side effects. Understanding the source and hidden mechanisms of drug side effects is found to be challenging in the drug development process. Here comes the significance of system biology approaches for inter connecting different scales of drug actions like drug-protein interactions, drug side effects towards side effect prediction for uncharacterized drugs. The emphasis of the study is to extend the systems approaches towards drug discovery for Hepatitis-C by using network analysis. We performed an extensive analysis to retrieve the network of targeted proteins and side effects on the basis of co-occurrence of drugs in protein-binding profiles and side effect profiles. The analysis of 12 drugs with 18 proteins and 14 side effects resulted in the extraction of many correlated sets. This led to a biologically relevant assessment regarding the relationship between drug- targeted proteins and side effects. The identified side effects can be considered as possible phenotypic outcomes by drugs targeting the proteins that appear in the same correlated set. This study is found to be useful in predicting potential side effects of drug candidate compounds based on their protein-binding profiles.