High-throughput screening (HTS) is a set of measurement techniques relevant in the fields of biology, chemistry and medicine. Since the publication of the human genome project, HTS has become a key technology allowing a researcher to quickly conduct millions of chemical, genetic and pharmacological tests. Results obtained from these tests are usually represented in form of networks and similar type of diagrams in order to facilitate biological interpretation. However, it is difficult to find tools which display and manage the massive number of biomarkers involved without crashing, requiring a lot of computer memory and showing a complex framework to understand and use. Therefore, this project specifies, designs and builds methods for the representation of large datasets from high throughput screening, concretely metabolomics and Gas Chromatography - Mass Spectrometry (GC-MS). The methodology is based on applying clustering algorithms to these datasets to visualize the networks they form at different scales. It uses external information from databases such as KEGG Pathways, Reactome and the Human Metabolome Database in order to include curated information of signalling pathways and reactions in human biology. Finally, a lightweight application is developed in order to display and manipulate both small and large networks, for the latter applying the mentioned methods. This program also includes extra functionalities which have been requested and specified by potential users through interviews.