The sheer volume of existing information and the anticipated explosion of data generated in the life sciences domain pose a major hurdle in drug discovery research. Although a significant proportion of this data is organized in a structured form, the relationship between these data and their interpretation has not been fully exploited. The application of semantic techniques to drug discovery will facilitate in extracting and understanding relationships, for instance between genes and diseases or compounds and side effects which are fundamental for drug discovery. The focus of this semantic approach is to build multiple ontologies that can help in representing the relationships between different domain information. By understanding the complex relationship between these data and eliminating unwanted information, the process of lead identification in drug discovery can be expedited. This can have a significant impact on drug discovery productivity in pharmaceutical companies. We have developed a prototype system, wherein we have exploited the relationships between drug targets, bioactive compounds and their chemical information to answer questions critical to speeding the process of lead identification.