Prediction of a novel or potential lead molecules for a therapeutic drug target without adverse effects is a challenging task in the drug designing, discovery, and development process. The systematic integration of multi-omics data from various data/knowledge bases through computational techniques enables to identify potential lead molecules and study the therapeutic properties. Over the last decades, several drug discoveries using multi-omics and huge dataset integration methods proven with successive results. In this paper, we present different types of computational approaches for prediction of potential lead molecules through the systems-level integration of multi-omics datasets.