Drug screening of novel compounds or repurposing existing ones is a major effort in finding therapies for cancer and other diseases. Currently, this is achieved through one of two approaches. The first generates high-throughput datasets where a biological model is treated with the compounds and their effect on the gene products measured. Alternatively, in-silico simulation of the impact of the compounds is predicted based on their compositions and the structure of their potential targets. Although these two approaches successfully discovered novel therapies and suggested mechanisms of action, other approaches are needed to speed up or improve the accuracy of their predictions. Using existing biological knowledge and computational methods could serve this purpose well. This project aims to organize biological knowledge of crucial cancer pathways into causal biological networks and integrate these networks with drug perturbation data. The output of this work will be in the form of a database to discover alternative mechanisms of existing and new drugs and case studies of the same nature. This work would supplement existing approaches for finding targets and repurposing existing drugs in multiple ways.

This is a work in progress. The goals of the project that have been achieved so far are