Computational drug repositioning is a recently emerged trend in discovering the drug-disease relationship. Currently, studies have been using drug repositioning techniques instead of the traditional drug discovery processes and drug repositioning molecular modeling tools such as AutoDock Vina, due to their reduced costs, time consumption, and high selectivity to the targeted disease. Drug repositioning is the reusing of either authorized or under-investigation drugs to give a new indication about drug function. One of the latest drug repositioning perspectives is the “Reverse Signature”, which depends on the counteracting of drugs to the genetic expression patterns of various diseases. Moreover, genetic-based drug repositioning is considered one of the most promising approaches for the future of medicinal chemistry treatment of cancer, because of the dependence of these approaches on different databases and platforms that were constructed by using various algorithms such as limma and EdgeR packages. The most commonly used are the Connectivity Map (CMap) and the Library of Integrated Cellular Signatures (LINCS), which are network-based platforms that gather the responses of various cell lines to a wide variety of drugs after the integration of heterogeneous databases, which include information about disease and phenotypic changes relationships, drug-disease effect after its administration, protein-protein interaction, and disease-pathway association. Despite the continuous exploration of computational-based repositioning tools, not all the recently developed tools and models are well-known to medicinal chemistry scientists. So, this chapter aims to collect and illustrate the recently developed transcription-based models, tools, and databases that aid in treating different cancer types.